Producer prices in industry, total - monthly data

Data - Eurostat

Author

François Geerolf

Info

source dataset .html .RData

eurostat

sts_inpp_m

 2024-06-20 2024-06-18

eurostat

sts_inppd_m

 2024-06-20 2024-06-08

eurostat

sts_inppnd_m

 2024-06-20 2024-06-08

Data on industry

source dataset .html .RData

ec

INDUSTRY

 2024-06-19 2023-10-01

eurostat

ei_isin_m

 2024-06-23 2024-06-08

eurostat

htec_trd_group4

 2024-06-23 2024-06-08

eurostat

nama_10_a64

 2024-06-24 2024-06-18

eurostat

nama_10_a64_e

 2024-06-23 2024-06-18

eurostat

namq_10_a10_e

 2024-06-24 2024-06-08

eurostat

road_eqr_carmot

 2024-06-24 2024-06-08

eurostat

sts_inpp_m

 2024-06-20 2024-06-18

eurostat

sts_inppd_m

 2024-06-20 2024-06-08

eurostat

sts_inpr_m

 2024-06-20 2024-06-08

eurostat

sts_intvnd_m

 2024-06-20 2024-06-08

fred

industry

 2024-06-20 2024-06-07

oecd

ALFS_EMP

 2024-04-16 2024-05-12

oecd

BERD_MA_SOF

 2024-04-16 2023-09-09

oecd

GBARD_NABS2007

 2024-04-16 2023-11-22

oecd

MEI_REAL

 2024-05-12 2024-05-03

oecd

MSTI_PUB

 2024-06-20 2023-10-04

oecd

SNA_TABLE4

 2024-06-20 2024-04-30

wdi

NV.IND.EMPL.KD

 2024-01-06 2024-04-14

wdi

NV.IND.MANF.CD

 2024-06-20 2024-06-09

wdi

NV.IND.MANF.ZS

 2024-01-06 2024-04-14

wdi

NV.IND.TOTL.KD

 2024-01-06 2024-04-14

wdi

NV.IND.TOTL.ZS

 2024-01-06 2024-04-14

wdi

SL.IND.EMPL.ZS

 2024-01-06 2024-04-14

wdi

TX.VAL.MRCH.CD.WT

 2024-01-06 2024-04-14

Info

Code 
include_graphics("https://ec.europa.eu/eurostat/statistics-explained/images/3/33/EU%2C_EA-19_Industrial_producer_prices%2C_total%2C_domestic_and_non-domestic_market%2C_2010_-_2022%2C_undadjusted_data_%282015_%3D_100%29_01-06-2022.png")

LAST_COMPILE

LAST_COMPILE
2024-06-24

Last

Code 
sts_inpp_m %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()
time Nobs
2024M05 1232

indic_bt

Code 
sts_inpp_m %>%
  left_join(indic_bt, by = "indic_bt") %>%
  group_by(indic_bt, Indic_bt) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
indic_bt Indic_bt Nobs
PRON Total output price index - in national currency 6791679

nace_r2

Code 
sts_inpp_m %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(nace_r2, Nace_r2) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

s_adj

Code 
sts_inpp_m %>%
  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) 6791679

unit

Code 
sts_inpp_m %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
unit Unit Nobs
PCH_PRE Percentage change on previous period 1567617
PCH_SM Percentage change compared to same period in previous year 1499542
I15 Index, 2015=100 1498240
I21 Index, 2021=100 1328863
I10 Index, 2010=100 897417

geo

Code 
sts_inpp_m %>%
  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 
sts_inpp_m %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

France, Germany, Italy

MIG_DCOG - MIG - durable consumer goods

Percentage change compared to same period in previous year - PCH_SM

All

Code 
sts_inpp_m %>%
  filter(nace_r2 == "MIG_DCOG",
         indic_bt == "PRON",
         unit == "PCH_SM",
         geo %in% c("FR", "DE", "IT")) %>%
  transmute(geo, time, values = values/100) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  month_to_date %>%
  ggplot() + ylab("MIG_DCOG - MIG - durable consumer goods") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_3flags +
  scale_y_continuous(breaks = 0.01*seq(-60, 300, 1),
                     labels = percent_format(a = 1))

MIG_ING - MIG - intermediate goods

Percentage change compared to same period in previous year - PCH_SM

All

Code 
sts_inpp_m %>%
  filter(nace_r2 == "MIG_ING",
         indic_bt == "PRON",
         unit == "PCH_SM",
         geo %in% c("FR", "DE", "IT")) %>%
  transmute(geo, time, values = values/100) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  month_to_date %>%
  ggplot() + ylab("MIG - intermediate goods") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_3flags +
  scale_y_continuous(breaks = 0.01*seq(-60, 300, 1),
                     labels = percent_format(a = 1))

MIG_CAG - MIG - capital goods

Percentage change compared to same period in previous year - PCH_SM

All

Code 
sts_inpp_m %>%
  filter(nace_r2 == "MIG_CAG",
         indic_bt == "PRON",
         unit == "PCH_SM",
         geo %in% c("FR", "DE", "IT")) %>%
  transmute(geo, time, values = values/100) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  month_to_date %>%
  ggplot() + ylab("MIG - capital goods") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_3flags +
  scale_y_continuous(breaks = 0.01*seq(-60, 300, 1),
                     labels = percent_format(a = 1))

D - Electricity, gas, steam and air conditioning supply

Percentage change compared to same period in previous year - PCH_SM

All

Code 
sts_inpp_m %>%
  filter(nace_r2 == "D",
         indic_bt == "PRON",
         unit == "PCH_SM",
         geo %in% c("FR", "DE", "IT")) %>%
  transmute(geo, time, values = values/100) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  month_to_date %>%
  ggplot() + ylab("Non-domestic output price index") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_3flags +
  scale_y_continuous(breaks = 0.01*seq(-60, 300, 10),
                     labels = percent_format(a = 1))

B - Mining and quarrying

Percentage change compared to same period in previous year - PCH_SM

All

Code 
sts_inpp_m %>%
  filter(nace_r2 == "B",
         indic_bt == "PRON",
         unit == "PCH_SM",
         geo %in% c("FR", "DE", "IT")) %>%
  transmute(geo, time, values = values/100) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  month_to_date %>%
  ggplot() + ylab("Mining and quarrying") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_3flags +
  scale_y_continuous(breaks = 0.01*seq(-60, 300, 10),
                     labels = percent_format(a = 1))

C - Manufacturing

Percentage change compared to same period in previous year - PCH_SM

All

Code 
sts_inpp_m %>%
  filter(nace_r2 == "C",
         indic_bt == "PRON",
         unit == "PCH_SM",
         geo %in% c("FR", "DE", "IT")) %>%
  transmute(geo, time, values = values/100) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  month_to_date %>%
  ggplot() + ylab("% change compared to same period in previous year") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_3flags +
  scale_y_continuous(breaks = 0.01*seq(-60, 300, 2),
                     labels = percent_format(a = 1))

2000-

Code 
sts_inpp_m %>%
  filter(nace_r2 == "C",
         indic_bt == "PRON",
         unit == "PCH_SM",
         geo %in% c("FR", "DE", "IT")) %>%
  transmute(geo, time, values = values/100) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  month_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  ggplot() + ylab("% change compared to same period in previous year") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_3flags +
  scale_y_continuous(breaks = 0.01*seq(-60, 300, 2),
                     labels = percent_format(a = 1))

2018-

Code 
sts_inpp_m %>%
  filter(nace_r2 == "C",
         indic_bt == "PRON",
         unit == "PCH_SM",
         geo %in% c("FR", "DE", "IT")) %>%
  transmute(geo, time, values = values/100) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  month_to_date %>%
  filter(date >= as.Date("2018-01-01")) %>%
  ggplot() + ylab("% change compared to same period in previous year") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_3flags +
  scale_y_continuous(breaks = 0.01*seq(-60, 300, 2),
                     labels = percent_format(a = 1))

Index

All

Code 
sts_inpp_m %>%
  filter(nace_r2 == "C",
         indic_bt == "PRON",
         unit == "I15",
         geo %in% c("FR", "DE", "IT")) %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2000M01"]) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  month_to_date %>%
  ggplot() + ylab("Non-domestic output price index - in national currency") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_3flags +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 10))

2000-

Code 
sts_inpp_m %>%
  filter(nace_r2 == "C",
         indic_bt == "PRON",
         unit == "I15",
         geo %in% c("FR", "DE", "IT")) %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2000M01"]) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  month_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  ggplot() + ylab("Total output price index - in national currency") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  add_3flags +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(-60, 300, 5))

2015-

Code 
sts_inpp_m %>%
  filter(nace_r2 == "C",
         indic_bt == "PRON",
         unit == "I15",
         geo %in% c("FR", "DE", "IT")) %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2015M01"]) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  month_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + scale_color_identity() + 
  add_3flags + ylab("Total output price index") + xlab("") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(-60, 300, 5))

2018-

Code 
sts_inpp_m %>%
  filter(nace_r2 == "C",
         indic_bt == "PRON",
         unit == "I15",
         geo %in% c("FR", "DE", "IT")) %>%
  select(geo, time, values) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[time == "2018M01"]) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  month_to_date %>%
  filter(date >= as.Date("2018-01-01")) %>%
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + scale_color_identity() + 
  add_3flags + ylab("Total output price index") + xlab("") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(-60, 300, 5))