Consumer Price Index - Urban Wage Earners and Clerical Workers (Current Series) - CW

Data - BLS

Author

François Geerolf

Info

LAST_DOWNLOAD

LAST_DOWNLOAD
2022-06-05

LAST_COMPILE

LAST_COMPILE
2024-06-20

Last

date Nobs
2022-04-01 62

cw.area

Code 
cw.area %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

cw.item

Code 
cw.item %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

cw.seasonal

Code 
cw.seasonal %>%
  {if (is_html_output()) print_table(.) else .}
seasonal_code seasonal_text
S Seasonally Adjusted
U Not Seasonally Adjusted

cw.periodicity

Code 
cw.periodicity %>%
  {if (is_html_output()) print_table(.) else .}
periodicity_code periodicity_name
R Monthly
S Semi-Annual

Longest Series (starting before 1950)

Code 
cw.series %>%
  filter(begin_year <= 1950) %>%
  arrange(begin_year) %>%
  select(series_id, series_title, begin_year, end_year) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Longest Series

All

Code 
cw.data.1.AllItems %>%
  bind_rows(cw.data.11.USFoodBeverage) %>%
  bind_rows(cw.data.12.USHousing) %>%
  bind_rows(cw.data.13.USApparel) %>%
  bind_rows(cw.data.14.USTransportation) %>%
  filter(series_id %in% c("CWUR0000SA0", "CWUR0000SAA", 
                          "CWUR0000SAF1", "CWUR0000SEHA")) %>%
  left_join(cw.series, by = "series_id") %>%
  left_join(cw.item, by = "item_code") %>%
  month_to_date() %>%
  ggplot(.) + theme_minimal() + xlab("") + 
  ylab("Consumer Price Index") +
  geom_line(aes(x = date, y = value, color = item_name)) +
  geom_rect(data = nber_recessions %>%
              filter(Trough >= as.Date("1914-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = seq(1910, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  scale_y_log10(breaks = c(seq(0, 50, 10), seq(100, 500, 50)),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.2, 0.80),
        legend.title = element_blank())

1914-1945

Code 
cw.data.1.AllItems %>%
  bind_rows(cw.data.11.USFoodBeverage) %>%
  bind_rows(cw.data.12.USHousing) %>%
  bind_rows(cw.data.13.USApparel) %>%
  bind_rows(cw.data.14.USTransportation) %>%
  filter(series_id %in% c("CWUR0000SA0", "CWUR0000SAA", 
                          "CWUR0000SAF1", "CWUR0000SEHA")) %>%
  left_join(cw.series, by = "series_id") %>%
  left_join(cw.item, by = "item_code") %>%
  month_to_date() %>%
  filter(date <= as.Date("1945-01-01")) %>%
  ggplot(.) + theme_minimal() + xlab("") + 
  ylab("Consumer Price Index") +
  geom_line(aes(x = date, y = value, color = item_name)) +
  geom_rect(data = nber_recessions %>%
              filter(Peak <= as.Date("1945-01-01"),
                     Trough >= as.Date("1914-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = seq(1910, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  scale_y_log10(breaks = seq(-10, 500, 10),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

1970-

Code 
cw.data.1.AllItems %>%
  bind_rows(cw.data.11.USFoodBeverage) %>%
  bind_rows(cw.data.12.USHousing) %>%
  bind_rows(cw.data.13.USApparel) %>%
  bind_rows(cw.data.14.USTransportation) %>%
  filter(series_id %in% c("CWUR0000SA0", "CWUR0000SAA", 
                          "CWUR0000SAF1", "CWUR0000SEHA")) %>%
  left_join(cw.series, by = "series_id") %>%
  left_join(cw.item, by = "item_code") %>%
  month_to_date() %>%
  filter(date >= as.Date("1970-01-01")) %>%
  group_by(item_name) %>%
  arrange(date) %>%
  mutate(value = 100*value/value[1]) %>%
  ggplot(.) + theme_minimal() + xlab("") + 
  ylab("Consumer Price Index") +
  geom_line(aes(x = date, y = value, color = item_name)) +
  geom_rect(data = nber_recessions %>%
              filter(Peak <= as.Date("2025-01-01"),
                     Trough >= as.Date("1970-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = seq(1910, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  scale_y_log10(breaks = seq(0, 1000, 50),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

1990-

Code 
cw.data.1.AllItems %>%
  bind_rows(cw.data.11.USFoodBeverage) %>%
  bind_rows(cw.data.12.USHousing) %>%
  bind_rows(cw.data.13.USApparel) %>%
  bind_rows(cw.data.14.USTransportation) %>%
  filter(series_id %in% c("CWUR0000SA0", "CWUR0000SAA", 
                          "CWUR0000SAF1", "CWUR0000SEHA")) %>%
  left_join(cw.series, by = "series_id") %>%
  left_join(cw.item, by = "item_code") %>%
  month_to_date() %>%
  filter(date >= as.Date("1990-01-01")) %>%
  group_by(item_name) %>%
  arrange(date) %>%
  mutate(value = 100*value/value[1]) %>%
  ggplot(.) + theme_minimal() + xlab("") + 
  ylab("Consumer Price Index") +
  geom_line(aes(x = date, y = value, color = item_name)) +
  geom_rect(data = nber_recessions %>%
              filter(Peak <= as.Date("2025-01-01"),
                     Trough >= as.Date("1990-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = seq(1910, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1000, 10),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())