En 2018, le salaire net moyen dans le secteur privé augmente de 0,4 % en euros constants - ip1828

Data - INSEE

Author

date

Code

ip1828 %>%
  group_by(date) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

date	Nobs
1996-01-01	3
1997-01-01	3
1998-01-01	3
1999-01-01	3
2000-01-01	3
2001-01-01	3
2002-01-01	3
2003-01-01	3
2004-01-01	3
2005-01-01	3
2006-01-01	3
2007-01-01	3
2008-01-01	3
2009-01-01	3
2010-01-01	3
2011-01-01	3
2012-01-01	3
2013-01-01	3
2014-01-01	3
2015-01-01	3
2016-01-01	3
2017-01-01	3
2018-01-01	3

variable

Code

ip1828 %>%
  group_by(variable, sheet) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

variable	sheet	Nobs
decile1	fig5	23
decile9	fig5	23
mediane	fig5	23

1998-2008, 2008-2018

Table

Code

ip1828 %>%
  mutate(year = year(date)) %>%
  select(-date) %>%
  filter(sheet == "fig5",
         year %in% c(1998, 2008, 2018)) %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(year, value) %>%
  print_table_conditional()

variable	1998	2008	2018
decile1	103.5	111.6	115.2
decile9	101.0	106.3	113.4
mediane	101.8	106.2	110.5

Niveau de vie

Decile 1, Mediane, Decile 9

IPC

Code

ip1828 %>%
  filter(sheet == "fig5",
         variable %in% c("decile1", "mediane", "decile9")) %>%
  group_by(variable) %>%
  mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
  ggplot() + ylab("Salaire net") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = value, color = variable)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  theme(legend.position = c(0.15, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 2),
                     labels = dollar_format(accuracy = 1, prefix = ""))

IPCH

Code

IPC_IPCH_adjustment <- `IPCH-IPC-2015-ensemble` %>%
  group_by(date) %>%
  summarise(OBS_VALUE = 100*OBS_VALUE[INDICATEUR == "IPCH"]/OBS_VALUE[INDICATEUR == "IPC"]) %>%
  filter(month(date) == 1,
         date <= as.Date("2019-01-01")) %>%
  select(date, IPC_IPCH_adjustment = OBS_VALUE)

ip1828 %>%
  filter(sheet == "fig5",
         variable %in% c("decile1", "mediane", "decile9")) %>%
  group_by(variable) %>%
  mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
  left_join(IPC_IPCH_adjustment, by = "date") %>%
  ggplot() + ylab("Salaire net (IPCH)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = 100*value/IPC_IPCH_adjustment, color = variable)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  theme(legend.position = c(0.15, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 2),
                     labels = dollar_format(accuracy = 1, prefix = ""))