Producer prices in industry, total - monthly data

Data - Eurostat

Author

François Geerolf

Info

source dataset Title .html .rData
eurostat sts_inpp_m Producer prices in industry, total - monthly data 2026-06-04 2026-04-26
eurostat sts_inppd_m Producer prices in industry, domestic market - monthly data 2026-06-04 2026-04-26
eurostat sts_inppnd_m Producer prices in industry, non domestic market - monthly data 2026-06-04 2026-05-28

Last observation: Monthly: 2026M03 (N = 4,611)

First observation: Monthly: 1976M01 (N = 89)

Last data update: 26 avr 2026, 20:45. Last compile: 04 jui 2026, 15:28

Structure

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
2026-06-04

Last

Code 
sts_inpp_m %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()
time Nobs
2026M03 4611

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 == "PRC_PRR",
         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, 2100, 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 == "PRC_PRR",
         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, 2100, 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 == "PRC_PRR",
         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, 2100, 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 == "PRC_PRR",
         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, 2100, 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 == "PRC_PRR",
         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, 2100, 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 == "PRC_PRR",
         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, 2100, 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 == "PRC_PRR",
         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, 2100, 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 == "PRC_PRR",
         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, 2100, 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 == "PRC_PRR",
         unit == "I21",
         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, 2100, 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 == "PRC_PRR",
         unit == "I21",
         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, 2100, 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 == "PRC_PRR",
         unit == "I21",
         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, 2100, 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 == "PRC_PRR",
         unit == "I21",
         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, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(-60, 300, 5))