Unit labour costs and labour productivity (employment based), Total economy

Data - OECD

Author

François Geerolf


Info

source dataset Title .html .rData
oecd ULC_EEQ Unit labour costs and labour productivity (employment based), Total economy 2025-11-17 2024-04-15

Data on productivity

source dataset Title .html .rData
oecd PDBI_I4 Productivity by industry (ISIC Rev.4) - PDBI_I4 2025-11-17 2024-04-15
oecd ULC_EEQ Unit labour costs and labour productivity (employment based), Total economy 2025-11-17 2024-04-15

Data on wages

source dataset Title .html .rData
eurostat earn_mw_cur Monthly minimum wages - bi-annual data 2025-12-25 2025-12-25
eurostat ei_lmlc_q Labour cost index, nominal value - quarterly data 2025-12-25 2025-12-25
eurostat lc_lci_lev Labour cost levels by NACE Rev. 2 activity 2025-12-25 2025-12-25
eurostat lc_lci_r2_q Labour cost index by NACE Rev. 2 activity - nominal value, quarterly data 2025-12-25 2025-12-24
eurostat nama_10_lp_ulc Labour productivity and unit labour costs 2025-12-25 2025-12-25
eurostat namq_10_lp_ulc Labour productivity and unit labour costs 2025-12-25 2025-12-25
eurostat tps00155 Minimum wages 2025-12-25 2025-12-25
fred wage Wage 2025-12-25 2025-12-25
ilo EAR_4MTH_SEX_ECO_CUR_NB_A Mean nominal monthly earnings of employees by sex and economic activity -- Harmonized series 2024-06-20 2023-06-01
ilo EAR_XEES_SEX_ECO_NB_Q Mean nominal monthly earnings of employees by sex and economic activity -- Harmonized series 2024-06-20 2023-06-01
oecd AV_AN_WAGE Average annual wages 2025-11-17 2025-12-21
oecd AWCOMP Taxing Wages - Comparative tables 2025-11-17 2023-09-09
oecd EAR_MEI Hourly Earnings (MEI) 2024-04-16 2024-04-16
oecd HH_DASH Household Dashboard 2025-11-17 2023-09-09
oecd MIN2AVE Minimum relative to average wages of full-time workers - MIN2AVE 2025-11-17 2023-09-09
oecd RMW Real Minimum Wages - RMW 2025-11-17 2024-03-12
oecd ULC_EEQ Unit labour costs and labour productivity (employment based), Total economy 2025-11-17 2024-04-15

LAST_COMPILE

LAST_COMPILE
2025-12-26

Last

obsTime FREQUENCY Nobs
2023 A 4
2022 A 138
2021 A 138
2023-Q4 Q 27
2023-Q3 Q 359
2023-Q2 Q 393

Number of observations

Code 
ULC_EEQ %>%
  left_join(ULC_EEQ_var$SUBJECT, by = c("SUBJECT")) %>%
  group_by(SUBJECT, Subject, MEASURE, FREQUENCY) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Data Structure

Code 
ULC_EEQ_var$VAR_DESC %>%
  {if (is_html_output()) print_table(.) else .}
id description
LOCATION Country
SUBJECT Subject
MEASURE Measure
FREQUENCY Frequency
TIME Time
OBS_VALUE Observation Value
TIME_FORMAT Time Format
OBS_STATUS Observation Status
UNIT Unit
POWERCODE Unit multiplier
REFERENCEPERIOD Reference period

SUBJECT

Code 
ULC_EEQ %>%
  left_join(ULC_EEQ_var$SUBJECT, by = "SUBJECT") %>%
  group_by(SUBJECT, Subject) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
SUBJECT Subject Nobs
ULQEUL01 Unit Labour Costs 22408
ULQELP01 GDP per person employed 18962
ULQECU01 Labour Compensation per employed person 18189

MEASURE

Code 
ULC_EEQ %>%
  left_join(ULC_EEQ_var$MEASURE, by = "MEASURE") %>%
  group_by(MEASURE, Measure) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
MEASURE Measure Nobs
IXOBSA Index, seasonally adjusted 15645
GPSA Quarterly change compared to previous quarter, seasonally adjusted 15421
GYSA Quarterly change on the same quarter of the previous year, seasonally adjusted 15161
IXOB Index 13332

FREQUENCY

Code 
ULC_EEQ %>%
  left_join(ULC_EEQ_var$FREQUENCY, by = "FREQUENCY") %>%
  group_by(FREQUENCY, Frequency) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
FREQUENCY Frequency Nobs
Q Quarterly 55045
A Annual 4514

TIME_FORMAT

Code 
ULC_EEQ %>%
  left_join(ULC_EEQ_var$TIME_FORMAT, by = "TIME_FORMAT") %>%
  group_by(TIME_FORMAT, Time_format) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
TIME_FORMAT Time_format Nobs
P3M Quarterly 55045
P1Y Annual 4514

LOCATION

Code 
ULC_EEQ %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Unit Labour Cost

France / Germany

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQEUL01",
         LOCATION %in% c("FRA", "DEU"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 200, 10)) +
  ylab("Unit Labour Cost") + xlab("")

France / Germany from 1990

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQEUL01",
         LOCATION %in% c("FRA", "DEU"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  filter(year(date) >= 1990) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + ylab("GDP per person employed") + xlab("") + add_2flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_identity() +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 200, 5))

France / Germany / United States

Unit Labour Costs - ULQEUL01

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQEUL01",
         LOCATION %in% c("FRA", "DEU", "USA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  filter(year(date) >= 1990) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("1996-01-01")]) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + ylab("GDP per person employed") + xlab("") + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_identity() +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 200, 5))

GDP per person employed - ULQELP01

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQELP01",
         LOCATION %in% c("FRA", "DEU", "USA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  filter(year(date) >= 1990) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("1996-01-01")]) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + ylab("GDP per person employed") + xlab("") + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_identity() +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 200, 5))

Labour Compensation per employed person - ULQECU01

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQECU01",
         LOCATION %in% c("FRA", "DEU", "USA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  filter(year(date) >= 1990) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("1996-01-01")]) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + ylab("Labour Compensation per employed person") + xlab("") + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_identity() +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 300, 10))

Euro area / United States

Unit Labour Costs - ULQEUL01

All

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQEUL01",
         LOCATION %in% c("EA19", "USA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  filter(year(date) >= 1990) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("1996-01-01")]) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + ylab("Unit Labour Cost") + xlab("") + add_2flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_identity() +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5))

1995-

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQEUL01",
         LOCATION %in% c("EA19", "USA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  filter(year(date) >= 1995) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("1995-01-01")]) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + ylab("Unit Labour Cost") + xlab("") + add_2flags +
  scale_x_date(breaks = seq(1921, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_identity() +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5))

1996-

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQEUL01",
         LOCATION %in% c("EA19", "USA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  filter(year(date) >= 1996) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("1996-01-01")]) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + ylab("Unit Labour Cost") + xlab("") + add_2flags +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_identity() +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5))

2017-

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQEUL01",
         LOCATION %in% c("EA19", "USA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  filter(year(date) >= 2017) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2017-01-01")]) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  
  theme_minimal() + ylab("Unit Labour Cost") + xlab("") + add_2flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_identity() +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 200, 5))

2020-

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQEUL01",
         LOCATION %in% c("EA19", "USA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  filter(year(date) >= 2020) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2020-01-01")]) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  
  theme_minimal() + ylab("Unit Labour Cost") + xlab("") + add_2flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_identity() +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 200, 5))

GDP per person employed - ULQELP01

All

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQELP01",
         LOCATION %in% c("EA19", "USA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  filter(year(date) >= 1990) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("1996-01-01")]) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  
  theme_minimal() + ylab("GDP per person employed") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 200, 5))

2017-

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQELP01",
         LOCATION %in% c("EA19", "USA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  filter(year(date) >= 2017) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2017-01-01")]) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  
  theme_minimal() + ylab("Unit Labour Cost") + xlab("") + add_2flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_identity() +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 200, 5))

2020-

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQELP01",
         LOCATION %in% c("EA19", "USA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  filter(year(date) >= 2020) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2020-01-01")]) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  
  theme_minimal() + ylab("Unit Labour Cost") + xlab("") + add_2flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_identity() +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 200, 5))

Labour Compensation per employed person - ULQECU01

All

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQECU01",
         LOCATION %in% c("EA19", "USA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  filter(year(date) >= 1990) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("1996-01-01")]) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  
  theme_minimal() + ylab("Labour Compensation per employed person") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 300, 10))

2017-

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQECU01",
         LOCATION %in% c("EA19", "USA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  filter(year(date) >= 2017) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2017-01-01")]) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  
  theme_minimal() + ylab("Labour Compensation per employed person") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 300, 10))

2019-Q4

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQECU01",
         LOCATION %in% c("EA19", "USA"),
         FREQUENCY == "Q") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Euro area", "US"),
         date = zoo::as.yearqtr(obsTime, format = "%Y-Q%q")) %>%
  filter(date >= zoo::as.yearqtr("2019 Q4")) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == zoo::as.yearqtr("2019 Q4")]) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  theme_minimal() + ylab("100 = 2019-Q4") + xlab("") +
  zoo::scale_x_yearqtr(format = "%Y-Q%q",
                       n = 15) +
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(breaks = seq(0, 300, 2))

2020-

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQECU01",
         LOCATION %in% c("EA19", "USA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  filter(year(date) >= 2020) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2020-01-01")]) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  
  theme_minimal() + ylab("Labour Compensation per employed person") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 300, 2))

Euro area vs. US

2017-Q2

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         LOCATION %in% c("EA19", "USA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$SUBJECT, by = "SUBJECT") %>%
  mutate(Subject = ifelse(SUBJECT == "ULQECU01", "Labour Compensation\nper person employed", Subject)) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Euro area", "US"),
         date = zoo::as.yearqtr(obsTime, format = "%Y-Q%q")) %>%
  filter(date >= zoo::as.yearqtr("2017 Q2")) %>%
  mutate(Subject = factor(Subject, levels = c("Unit Labour Costs ",
                                              "Labour Compensation\nper person employed",
                                              "GDP per person employed"))) %>%
  group_by(Location, Subject) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == zoo::as.yearqtr("2017 Q2")]) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  theme_minimal() + ylab("100 = 2019-Q4") + xlab("") +
  zoo::scale_x_yearqtr(format = "%Y-Q%q",
                       breaks = zoo::as.yearqtr(c("2017 Q2", "2017 Q4", "2018 Q2", "2018 Q4",
                                                  "2019 Q2", "2019 Q4", "2020 Q2", "2020 Q4",
                                                  "2021 Q2", "2021 Q4", "2022 Q2", "2022 Q4",
                                                  "2023 Q2"))) +
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = "top",
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(breaks = seq(0, 300, 2)) +
  facet_wrap(~ Subject)

2019-Q4

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         LOCATION %in% c("EA19", "USA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$SUBJECT, by = "SUBJECT") %>%
  mutate(Subject = ifelse(SUBJECT == "ULQECU01", "Labour Compensation\nper person employed", Subject)) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Euro area", "US"),
         date = zoo::as.yearqtr(obsTime, format = "%Y-Q%q")) %>%
  filter(date >= zoo::as.yearqtr("2019 Q4")) %>%
  mutate(Subject = factor(Subject, levels = c("Unit Labour Costs ",
                                              "Labour Compensation\nper person employed",
                                              "GDP per person employed"))) %>%
  group_by(Location, Subject) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == zoo::as.yearqtr("2019 Q4")]) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  theme_minimal() + ylab("100 = 2019-Q4") + xlab("") +
  zoo::scale_x_yearqtr(format = "%Y-Q%q",
                       breaks = zoo::as.yearqtr(c("2019 Q4", "2020 Q2", "2020 Q4",
                                                  "2021 Q2", "2021 Q4", "2022 Q2",
                                                  "2022 Q4", "2023 Q2"))) +
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = "top",
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(breaks = seq(0, 300, 2)) +
  facet_wrap(~ Subject)

France, US, Eurozone

2019-Q4

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         LOCATION %in% c("EA19", "USA", "FRA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$SUBJECT, by = "SUBJECT") %>%
  mutate(Subject = ifelse(SUBJECT == "ULQECU01", "Labour Compensation\nper person employed", Subject)) %>%
  mutate(Location = case_when(LOCATION == "EA19" ~ "Euro area",
                              LOCATION == "USA" ~ "United States",
                              LOCATION == "FRA" ~ "France",
                              T ~ "NA"),
         date = zoo::as.yearqtr(obsTime, format = "%Y-Q%q")) %>%
  filter(date >= zoo::as.yearqtr("2019 Q4")) %>%
  mutate(Subject = factor(Subject, levels = c("Unit Labour Costs ",
                                              "Labour Compensation\nper person employed",
                                              "GDP per person employed"))) %>%
  group_by(Location, Subject) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == zoo::as.yearqtr("2019 Q4")]) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  theme_minimal() + ylab("100 = 2019-Q4") + xlab("") + add_9flags +
  zoo::scale_x_yearqtr(format = "%Y-Q%q",
                       breaks = zoo::as.yearqtr(c("2019 Q4", "2020 Q2", "2020 Q4",
                                                  "2021 Q2", "2021 Q4", "2022 Q2",
                                                  "2022 Q4", "2023 Q2"))) +
  scale_color_manual(values = c("#FFCC00", "#002395", "#B22234")) +
  theme(legend.position = "top",
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(breaks = seq(0, 300, 2)) +
  facet_wrap(~ Subject)

2000-

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQELP01",
         LOCATION %in% c("EA19", "USA", "FRA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  filter(year(date) >= 2008) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2008-01-01")]) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = color2) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  
  theme_minimal() + ylab("GDP Per person employed") + xlab("") + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_identity() +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 200, 5))

2017-

Code 
ULC_EEQ %>%
  filter(MEASURE == "IXOBSA",
         SUBJECT == "ULQELP01",
         LOCATION %in% c("EA19", "USA", "FRA"),
         FREQUENCY == "Q") %>%
  left_join(ULC_EEQ_var$LOCATION, by = "LOCATION") %>%
  quarter_to_date %>%
  filter(year(date) >= 2017) %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2017-01-01")]) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = color2) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  
  theme_minimal() + ylab("GDP Per person employed") + xlab("") + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_identity() +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 200, 5))