Data - BLS
Last observation: 2022 (N = 173444)
First observation: 1939 (N = 1292)
Last data update: 02 août 2026, 08:50
Last compile: 05 sept. 2026, 01:26
Example: CES7072250003: verage hourly earnings of all employees, restaurants and other eating places, seasonally adjusted
ce.data.0.AllCESSeries |>
left_join(ce.series, by = "series_id") |>
left_join(ce.industry, by = "industry_code") |>
group_by(industry_code, industry_name) |>
summarise(Nobs = n()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}ce.data.0.AllCESSeries |>
left_join(ce.series, by = "series_id") |>
left_join(ce.supersector, by = "supersector_code") |>
group_by(supersector_code, supersector_name) |>
summarise(Nobs = n()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}ce.data.0.AllCESSeries |>
left_join(ce.series, by = "series_id") |>
left_join(ce.datatype, by = "data_type_code") |>
group_by(data_type_code, data_type_text) |>
summarise(Nobs = n()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}ce.data.0.AllCESSeries |>
left_join(ce.series, by = "series_id") |>
rename(seasonal_code = seasonal) |>
left_join(ce.seasonal, by = "seasonal_code") |>
group_by(seasonal_code, seasonal_text) |>
summarise(Nobs = n()) %>%
{if (is_html_output()) print_table(.) else .}| seasonal_code | seasonal_text | Nobs |
|---|---|---|
| S | Seasonally Adjusted | 3901660 |
| U | Not Seasonally Adjusted | 4210344 |
ce.data.0.AllCESSeries |>
left_join(ce.series, by = "series_id") |>
group_by(series_id, series_title) |>
summarise(Nobs = n()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}ce.data.0.AllCESSeries |>
group_by(year) |>
summarise(Nobs = n()) |>
arrange(desc(year)) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}`ce.data.0.AllCESSeries` |>
filter(series_id == "CES3000000008") |>
month_to_date() |>
mutate(wage_inflation = (value/lag(value, 12) - 1)) |>
ggplot() + ylab("Wage Inflation") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = wage_inflation)) +
scale_y_continuous(breaks = seq(-0.2, 0.4, 0.02),
labels = percent_format(acc = 1)) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2020, 5), "-01-01")),
labels = date_format("%Y"))
`ce.data.0.AllCESSeries` |>
filter(series_id == "CES3000000008") |>
month_to_date() |>
mutate(wage_inflation = (value/lag(value, 12) - 1)) |>
filter(date >= as.Date("2000-01-01")) |>
ggplot() + ylab("Wage Inflation") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = wage_inflation)) +
scale_y_continuous(breaks = seq(-0.2, 0.4, 0.01),
labels = percent_format(acc = 1)) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2022, 2), "-01-01")),
labels = date_format("%Y"))
ce.series |>
filter(industry_code %in% c(30000000, 20000000, 40000000),
data_type_code == 3,
seasonal == "S") |>
left_join(`ce.data.0.AllCESSeries`, by = "series_id") |>
left_join(ce.industry, by = "industry_code") |>
month_to_date() |>
mutate(wage_inflation = (value/lag(value, 12) - 1)) |>
filter(date >= as.Date("2008-01-01")) |>
ggplot() + ylab("Nominal Wage Inflation") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = wage_inflation, color = industry_name)) +
theme(legend.title = element_blank(),
legend.position = c(0.4, 0.9)) +
scale_y_continuous(breaks = seq(-0.2, 0.4, 0.01),
labels = percent_format(acc = 1)) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2022, 2), "-01-01")),
labels = date_format("%Y")) +
scale_color_manual(values = viridis(4)[1:3]) +
geom_hline(yintercept = 0, linetype = "dashed")
ce.series |>
filter(industry_code %in% c(30000000, 20000000, 40000000),
data_type_code == 3,
seasonal == "S") |>
left_join(`ce.data.0.AllCESSeries`, by = "series_id") |>
left_join(ce.industry, by = "industry_code") |>
month_to_date() |>
mutate(wage_inflation = (value/lag(value, 12) - 1)) |>
filter(date >= as.Date("2014-01-01")) |>
ggplot() + ylab("Nominal Wage Inflation") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = wage_inflation, color = industry_name)) +
theme(legend.title = element_blank(),
legend.position = c(0.4, 0.9)) +
scale_y_continuous(breaks = seq(-0.2, 0.4, 0.01),
labels = percent_format(acc = 1)) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2022, 1), "-01-01")),
labels = date_format("%Y")) +
scale_color_manual(values = viridis(4)[1:3]) +
geom_hline(yintercept = 0, linetype = "dashed")
ce.series |>
filter(industry_code %in% c(6000000, 7000000, 5000000, 8000000),
data_type_code == 3,
seasonal == "S") |>
left_join(`ce.data.0.AllCESSeries`, by = "series_id") |>
left_join(ce.industry, by = "industry_code") |>
month_to_date() |>
mutate(wage_inflation = (value/lag(value, 12) - 1)) |>
filter(date >= as.Date("2008-01-01")) |>
ggplot() + ylab("Nominal Wage Inflation") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = wage_inflation, color = industry_name)) +
theme(legend.title = element_blank(),
legend.position = c(0.4, 0.9)) +
scale_y_continuous(breaks = seq(-0.2, 0.4, 0.01),
labels = percent_format(acc = 1)) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2022, 2), "-01-01")),
labels = date_format("%Y")) +
scale_color_manual(values = viridis(4)[1:3]) +
geom_hline(yintercept = 0, linetype = "dashed")
ce.series |>
filter(industry_code %in% c(6000000, 7000000, 5000000, 8000000),
data_type_code == 3,
seasonal == "S") |>
left_join(`ce.data.0.AllCESSeries`, by = "series_id") |>
left_join(ce.industry, by = "industry_code") |>
month_to_date() |>
mutate(wage_inflation = (value/lag(value, 12) - 1)) |>
filter(date >= as.Date("2014-01-01")) |>
ggplot() + ylab("Nominal Wage Inflation") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = wage_inflation, color = industry_name)) +
theme(legend.title = element_blank(),
legend.position = c(0.4, 0.9)) +
scale_y_continuous(breaks = seq(-0.2, 0.4, 0.01),
labels = percent_format(acc = 1)) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2022, 1), "-01-01")),
labels = date_format("%Y")) +
scale_color_manual(values = viridis(4)[1:3]) +
geom_hline(yintercept = 0, linetype = "dashed")
ce.series |>
filter(industry_code %in% c(70721110, 70722511, 70722513),
data_type_code == 3,
seasonal == "S") |>
left_join(`ce.data.0.AllCESSeries`, by = "series_id") |>
left_join(ce.industry, by = "industry_code") |>
month_to_date() |>
mutate(wage_inflation = (value/lag(value, 12) - 1)) |>
filter(date >= as.Date("2008-01-01")) |>
ggplot() + ylab("Nominal Wage Inflation") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = wage_inflation, color = industry_name)) +
theme(legend.title = element_blank(),
legend.position = c(0.4, 0.9)) +
scale_y_continuous(breaks = seq(-0.2, 0.4, 0.01),
labels = percent_format(acc = 1)) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2022, 2), "-01-01")),
labels = date_format("%Y")) +
scale_color_manual(values = viridis(4)[1:3]) +
geom_hline(yintercept = 0, linetype = "dashed")
ce.series |>
filter(industry_code %in% c(70721110, 70722511, 70722513),
data_type_code == 3,
seasonal == "S") |>
left_join(`ce.data.0.AllCESSeries`, by = "series_id") |>
left_join(ce.industry, by = "industry_code") |>
month_to_date() |>
mutate(wage_inflation = (value/lag(value, 12) - 1)) |>
filter(date >= as.Date("2014-01-01")) |>
ggplot() + ylab("Average hourly earnings of all employees Inflation") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = wage_inflation, color = industry_name)) +
theme(legend.title = element_blank(),
legend.position = c(0.4, 0.9)) +
scale_y_continuous(breaks = seq(-0.2, 0.4, 0.01),
labels = percent_format(acc = 1)) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2022, 1), "-01-01")),
labels = date_format("%Y")) +
scale_color_manual(values = viridis(4)[1:3]) +
geom_hline(yintercept = 0, linetype = "dashed")
ce.series |>
filter(industry_code %in% c(70721110, 70722511, 70722513),
data_type_code == 3,
seasonal == "S") |>
left_join(`ce.data.0.AllCESSeries`, by = "series_id") |>
left_join(ce.industry, by = "industry_code") |>
month_to_date() |>
mutate(wage_inflation = (value/lag(value, 12) - 1)) |>
filter(date >= as.Date("2020-10-01")) |>
select(date, industry_name, value) |>
spread(industry_name, value) |>
arrange(desc(date)) |>
print_table_conditional()| date | Full-service restaurants | Hotels and motels, except casino hotels | Limited-service restaurants |
|---|---|---|---|
| 2022-07-01 | 20.16 | 22.41 | 16.20 |
| 2022-06-01 | 20.01 | 22.34 | 16.10 |
| 2022-05-01 | 19.87 | 22.16 | 16.07 |
| 2022-04-01 | 19.75 | 21.99 | 16.02 |
| 2022-03-01 | 19.62 | 21.91 | 15.97 |
| 2022-02-01 | 19.52 | 21.67 | 15.87 |
| 2022-01-01 | 19.37 | 21.71 | 15.84 |
| 2021-12-01 | 19.54 | 21.72 | 15.80 |
| 2021-11-01 | 19.35 | 21.58 | 15.73 |
| 2021-10-01 | 19.23 | 22.38 | 15.52 |
| 2021-09-01 | 19.04 | 20.47 | 15.46 |
| 2021-08-01 | 18.84 | 20.77 | 15.33 |
| 2021-07-01 | 18.71 | 20.27 | 15.12 |
| 2021-06-01 | 18.46 | 20.07 | 14.96 |
| 2021-05-01 | 18.11 | 19.62 | 14.75 |
| 2021-04-01 | 17.88 | 19.25 | 14.57 |
| 2021-03-01 | 17.62 | 18.86 | 14.28 |
| 2021-02-01 | 17.31 | 18.64 | 14.27 |
| 2021-01-01 | 17.12 | 18.52 | 14.12 |
| 2020-12-01 | 16.88 | 18.43 | 14.10 |
| 2020-11-01 | 16.95 | 17.98 | 13.90 |
| 2020-10-01 | 17.05 | 18.23 | 13.82 |
70721110