Final consumption aggregates - namq_10_fcs

Data - Eurostat

Author

François Geerolf

Info

LAST_DOWNLOAD

Code 
tibble(LAST_DOWNLOAD = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/namq_10_fcs.RData")$mtime)) %>%
  print_table_conditional()
LAST_DOWNLOAD
2024-10-08

LAST_COMPILE

LAST_COMPILE
2024-11-22

Last

Code 
namq_10_fcs %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()
time Nobs
2024Q2 5414

na_item

Code 
namq_10_fcs %>%
  left_join(na_item, by = "na_item") %>%
  group_by(na_item, Na_item) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
na_item Na_item Nobs
P31_S14 Final consumption expenditure of households 135818
P311_S14 Final consumption expenditure of households, durable goods 129228
P312N_S14 Final consumption expenditure of households, semi-durable goods, non-durable goods and services 129228
P312_S14 Final consumption expenditure of households, semi-durable goods 106379
P313_S14 Final consumption expenditure of households, non-durable goods 106379
P314_S14 Final consumption expenditure of households, services 106379

s_adj

Code 
namq_10_fcs %>%
  left_join(s_adj, by = "s_adj") %>%
  group_by(s_adj, S_adj) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
s_adj S_adj Nobs
NSA Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) 324422
SCA Seasonally and calendar adjusted data 293178
CA Calendar adjusted data, not seasonally adjusted data 65886
SA Seasonally adjusted data, not calendar adjusted data 29925

unit

Code 
namq_10_fcs %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
unit Unit Nobs
CLV15_MEUR Chain linked volumes (2015), million euro 50080
CLV15_MNAC Chain linked volumes (2015), million units of national currency 50080
CLV_I15 Chain linked volumes, index 2015=100 50080
CP_MEUR Current prices, million euro 49552
CP_MNAC Current prices, million units of national currency 49552
CLV10_MEUR Chain linked volumes (2010), million euro 48916
CLV10_MNAC Chain linked volumes (2010), million units of national currency 48916
CLV_I10 Chain linked volumes, index 2010=100 48916
CLV05_MEUR Chain linked volumes (2005), million euro 48028
CLV05_MNAC Chain linked volumes (2005), million units of national currency 48028
CLV_I05 Chain linked volumes, index 2005=100 48028
PC_GDP Percentage of gross domestic product (GDP) 47986
CLV_PCH_SM Chain linked volumes, percentage change compared to same period in previous year 47244
PYP_MEUR Previous year prices, million euro 27910
PYP_MNAC Previous year prices, million units of national currency 27910
CLV_PCH_PRE Chain linked volumes, percentage change on previous period 22185

geo

Code 
namq_10_fcs %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

time

Code 
namq_10_fcs %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  print_table_conditional()

France

Table

Code 
namq_10_fcs %>%
  filter(geo == "FR",
         time == "2021Q4",
         s_adj == "NSA") %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(unit, by = "unit") %>%
  left_join(na_item, by = "na_item") %>%
  mutate(Na_item = paste0(na_item, " - ", Na_item)) %>%
  select(-na_item) %>%
  spread(Na_item, values) %>%
  print_table_conditional()
unit Unit P31_S14 - Final consumption expenditure of households P311_S14 - Final consumption expenditure of households, durable goods P312_S14 - Final consumption expenditure of households, semi-durable goods P312N_S14 - Final consumption expenditure of households, semi-durable goods, non-durable goods and services P313_S14 - Final consumption expenditure of households, non-durable goods P314_S14 - Final consumption expenditure of households, services
CLV_I05 Chain linked volumes, index 2005=100 122.820 146.760 114.198 120.678 112.609 126.470
CLV_I10 Chain linked volumes, index 2010=100 114.269 122.326 112.157 113.597 108.092 116.907
CLV_I15 Chain linked volumes, index 2015=100 110.134 119.906 110.762 109.296 107.287 110.057
CLV_PCH_SM Chain linked volumes, percentage change compared to same period in previous year 7.400 -1.200 8.300 8.200 0.300 13.100
CLV05_MEUR Chain linked volumes (2005), million euro 288094.300 33302.800 28008.500 255686.100 75416.900 152238.500
CLV05_MNAC Chain linked volumes (2005), million units of national currency 288094.300 33302.800 28008.500 255686.100 75416.900 152238.500
CLV10_MEUR Chain linked volumes (2010), million euro 306706.300 29478.800 28530.900 277526.000 83217.300 165870.200
CLV10_MNAC Chain linked volumes (2010), million units of national currency 306706.300 29478.800 28530.900 277526.000 83217.300 165870.200
CLV15_MEUR Chain linked volumes (2015), million euro 319296.700 28012.300 29188.700 291331.700 89658.600 172384.700
CLV15_MNAC Chain linked volumes (2015), million units of national currency 319296.700 28012.300 29188.700 291331.700 89658.600 172384.700
CP_MEUR Current prices, million euro 341598.900 28056.700 29887.300 313542.200 101783.200 181871.600
CP_MNAC Current prices, million units of national currency 341598.900 28056.700 29887.300 313542.200 101783.200 181871.600
PC_GDP Percentage of gross domestic product (GDP) 51.400 4.200 4.500 47.200 15.300 27.400
PYP_MEUR Previous year prices, million euro 333721.400 27104.100 28845.700 306617.300 97996.400 179775.200
PYP_MNAC Previous year prices, million units of national currency 333721.400 27104.100 28845.700 306617.300 97996.400 179775.200

France, Germany, Spain, Italy, Netherlands

All

Consumption

Code 
namq_10_fcs %>%
  filter(geo %in% c("FR", "EA", "ES", "IT", "DE"),
         unit == "PC_GDP",
         s_adj == "NSA",
         na_item == "P31_S14") %>%
  quarter_to_date %>%
  mutate(values = values / 100) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "EA", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_4flags + xlab("") + 
  ylab("Final consumption expenditure of households (% of GDP)") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2022, 5), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-30, 100, 2),
                labels = percent_format(a = 1))

1995-

Consumption

Code 
namq_10_fcs %>%
  filter(geo %in% c("FR", "NL", "ES", "IT", "DE"),
         unit == "PC_GDP",
         s_adj == "NSA",
         na_item == "P31_S14") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  mutate(values = values / 100) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_4flags + xlab("") + 
  ylab("Final consumption expenditure of households (% of GDP)") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2022, 5), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-30, 100, 2),
                labels = percent_format(a = 1))

Durable goods

Code 
namq_10_fcs %>%
  filter(geo %in% c("FR", "NL", "ES", "IT", "DE"),
         unit == "PC_GDP",
         s_adj == "NSA",
         na_item == "P311_S14") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  mutate(values = values / 100) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_4flags + xlab("") + 
  ylab("Durable goods (% of GDP)") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2022, 5), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-30, 100, 1),
                labels = percent_format(a = 1))

Non-durable goods

Code 
namq_10_fcs %>%
  filter(geo %in% c("FR", "NL", "ES", "IT", "DE"),
         unit == "PC_GDP",
         s_adj == "NSA",
         na_item == "P313_S14") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  mutate(values = values / 100) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_4flags + xlab("") + 
  ylab("Non-durable goods (% of GDP)") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2022, 5), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-30, 100, 1),
                labels = percent_format(a = 1))

Services

Code 
namq_10_fcs %>%
  filter(geo %in% c("FR", "NL", "ES", "IT", "DE"),
         unit == "PC_GDP",
         s_adj == "NSA",
         na_item == "P314_S14") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  mutate(values = values / 100) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal()  + add_4flags + xlab("") + 
  ylab("Services (% of GDP)") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2022, 5), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-30, 100, 1),
                labels = percent_format(a = 1))