The Atlanta Fed’s Wage Growth Tracker - wage-growth-data
Data - FRB
François Geerolf
Info
LAST_DOWNLOAD
| LAST_DOWNLOAD |
|---|
| 2024-06-07 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-06-08 |
Last
| date | Nobs |
|---|---|
| 2024-03-01 | 16 |
date
variable
| variable | Nobs |
|---|---|
| Age 25-54 | 327 |
| College degree | 327 |
| Female | 327 |
| Full-time | 327 |
| Job Stayer | 327 |
| Job Switcher | 327 |
| Lower 1/2 of wage distn | 327 |
| Male | 327 |
| Overall | 327 |
| Overall: 25/20 trimmed mean | 327 |
| Overall: Weekly Basis | 327 |
| Overall: Weighted | 327 |
| Overall: Weighted 97 | 327 |
| Paid Hourly | 327 |
| Services | 327 |
| Upper 1/2 of wage distn | 327 |
Leisure and Hospitality, Overall
Nominal
`wage-growth-data-industry` %>%
filter(variable %in% c("Leisure and hospitality", "Overall")) %>%
ggplot(.) + geom_line(aes(x = date, y = value/100, color = variable)) +
theme_minimal() + ylab("Nominal Wage Growth (%)") + xlab("") +
theme(legend.title = element_blank(),
legend.position = c(0.85, 0.9)) +
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1993-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_color_manual(values = viridis(3)[1:2]) +
scale_x_date(breaks = seq(1930, 2022, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 700, 1),
labels = scales::percent_format(accuracy = 1))
Real
`wage-growth-data-industry` %>%
filter(variable %in% c("Leisure and hospitality", "Overall")) %>%
left_join(inflation, by = "date") %>%
mutate(value = value - inflation) %>%
ggplot(.) + geom_line(aes(x = date, y = value/100, color = variable)) +
theme_minimal() + ylab("Nominal Wage Growth (%)") + xlab("") +
theme(legend.title = element_blank(),
legend.position = c(0.85, 0.9)) +
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1993-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_color_manual(values = viridis(3)[1:2]) +
scale_x_date(breaks = seq(1930, 2022, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 700, 1),
labels = scales::percent_format(accuracy = 1)) +
geom_hline(yintercept = 0, linetype = "dashed")
Services, Unweighted overall
Nominal
`wage-growth-data` %>%
filter(variable %in% c("Services", "Weighted Overall")) %>%
ggplot(.) + geom_line(aes(x = date, y = value/100, color = variable)) +
theme_minimal() + ylab("Nominal Wage Growth (%)") + xlab("") +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.2)) +
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1993-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_color_manual(values = viridis(3)[1:2]) +
scale_x_date(breaks = seq(1930, 2022, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 700, 1),
labels = scales::percent_format(accuracy = 1))
Real
`wage-growth-data` %>%
filter(variable %in% c("Services", "Weighted Overall")) %>%
left_join(inflation, by = "date") %>%
mutate(value = value - inflation) %>%
ggplot(.) + geom_line(aes(x = date, y = value/100, color = variable)) +
theme_minimal() + ylab("Nominal Wage Growth (%)") + xlab("") +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.2)) +
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1993-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_color_manual(values = viridis(3)[1:2]) +
scale_x_date(breaks = seq(1930, 2022, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 700, 1),
labels = scales::percent_format(accuracy = 1)) +
geom_hline(yintercept = 0, linetype = "dashed")
Job-stayer, Job-switcher
Nominal
`wage-growth-data` %>%
filter(variable %in% c("Job Stayer", "Job Switcher")) %>%
ggplot(.) + geom_line(aes(x = date, y = value/100, color = variable)) +
theme_minimal() + ylab("Nominal Wage Growth (%)") + xlab("") +
theme(legend.title = element_blank(),
legend.position = c(0.85, 0.9)) +
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1993-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_color_manual(values = viridis(3)[1:2]) +
scale_x_date(breaks = seq(1930, 2022, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 700, 1),
labels = scales::percent_format(accuracy = 1))
Real
`wage-growth-data` %>%
filter(variable %in% c("Job Stayer", "Job Switcher")) %>%
left_join(inflation, by = "date") %>%
mutate(value = value - inflation) %>%
ggplot(.) + geom_line(aes(x = date, y = (value)/100, color = variable)) +
theme_minimal() + ylab("Real Wage Growth (%)") + xlab("") +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.2)) +
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1993-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_color_manual(values = viridis(3)[1:2]) +
scale_x_date(breaks = seq(1930, 2022, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 700, 1),
labels = scales::percent_format(accuracy = 1)) +
geom_hline(yintercept = 0, linetype = "dashed")
Full-time, Unweighted Overall
`wage-growth-data` %>%
filter(variable %in% c("Unweighted Overall", "Full-time")) %>%
ggplot(.) + geom_line(aes(x = date, y = value/100, color = variable)) +
theme_minimal() + ylab("Wage Growth (%)") + xlab("") +
theme(legend.title = element_blank(),
legend.position = c(0.85, 0.9)) +
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1993-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_color_manual(values = viridis(3)[1:2]) +
scale_x_date(breaks = seq(1930, 2022, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 700, 1),
labels = scales::percent_format(accuracy = 1))
Unweighted Overall
`wage-growth-data` %>%
filter(variable == "Unweighted Overall") %>%
ggplot(.) + geom_line(aes(x = date, y = value/100)) +
theme_minimal() + ylab("Unweighted Overall Wage Growth (%)") + xlab("") +
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1993-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_x_date(breaks = seq(1930, 2022, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 700, 1),
labels = scales::percent_format(accuracy = 1))
Services, Unweighted Overall
`wage-growth-data` %>%
filter(variable == "Services") %>%
ggplot(.) + geom_line(aes(x = date, y = value/100)) +
theme_minimal() + ylab("Services Wage Growth (%)") + xlab("") +
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1993-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_x_date(breaks = seq(1930, 2022, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 700, 1),
labels = scales::percent_format(accuracy = 1))
Full-time
`wage-growth-data` %>%
filter(variable == "Full-time") %>%
ggplot(.) + geom_line(aes(x = date, y = value/100)) +
theme_minimal() + ylab("Full-time Wage Growth (%)") + xlab("") +
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1993-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_x_date(breaks = seq(1930, 2022, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 700, 1),
labels = scales::percent_format(accuracy = 1))