Consumer price indices (CPIs)

Data - OECD

Author

François Geerolf

Info

source dataset .html .RData
oecd PRICES_ALL 2024-11-06 2025-04-02

Data on inflation

Title source dataset .html .RData
Consumer Price Index bis CPI 2024-12-29 2022-01-20
Consumer Expectations Survey ecb CES 2025-03-31 2025-03-31
Final consumption expenditure of households by consumption purpose (COICOP 3 digit) eurostat nama_10_co3_p3 2025-03-20 2025-03-20
HICP - country weights eurostat prc_hicp_cow 2025-02-01 2024-10-08
Contributions to euro area annual inflation (in percentage points) eurostat prc_hicp_ctrb 2025-02-01 2024-10-08
HICP - item weights eurostat prc_hicp_inw 2025-01-26 2025-03-09
HICP (2015 = 100) - monthly data (annual rate of change) eurostat prc_hicp_manr 2025-04-02 2025-03-31
HICP (2015 = 100) - monthly data (index) eurostat prc_hicp_midx 2025-03-31 2025-03-31
HICP (2015 = 100) - monthly data (monthly rate of change) eurostat prc_hicp_mmor 2025-02-01 2025-03-09
Purchasing power parities (PPPs), price level indices and real expenditures for ESA 2010 aggregates eurostat prc_ppp_ind 2025-02-01 2024-10-08
Producer prices in industry, total - monthly data eurostat sts_inpp_m 2024-06-24 2025-03-19
Producer prices in industry, domestic market - monthly data eurostat sts_inppd_m 2025-03-19 2025-03-19
Producer prices in industry, non domestic market - monthly data eurostat sts_inppnd_m 2024-06-24 2025-03-19
Consumer Price Index fred cpi 2025-04-01 2025-04-01
Inflation fred inflation 2025-04-01 2025-04-01
Consumer Price Index - CPI imf CPI 2024-06-20 2020-03-13
Producer Prices - MEI_PRICES_PPI oecd MEI_PRICES_PPI 2024-09-15 2024-04-15
2017 PPP Benchmark results oecd PPP2017 2024-04-16 2023-07-25
Consumer price indices (CPIs) oecd PRICES_CPI 2024-04-16 2024-04-15
Inflation, consumer prices (annual %) wdi FP.CPI.TOTL.ZG 2023-01-15 2025-03-09
Inflation, GDP deflator (annual %) wdi NY.GDP.DEFL.KD.ZG 2025-03-09 2025-03-09

Last

Monthly

obsTime Nobs
2025-03 93

Quarterly

obsTime Nobs
2025-Q1 69

Annual

obsTime Nobs
2025 152

FREQ

Code 
PRICES_ALL %>%
  left_join(FREQ, by = "FREQ") %>%
  group_by(FREQ, Freq) %>%
  summarise(Nobs = sum(!is.na(obsValue))) %>%
  arrange(-Nobs) %>%
  print_table_conditional
FREQ Freq Nobs
M Monthly 2063241
Q Quarterly 430296
A Annual 202937

MEASURE

Code 
PRICES_ALL %>%
  left_join(MEASURE, by = "MEASURE") %>%
  group_by(MEASURE, Measure) %>%
  summarise(Nobs = sum(!is.na(obsValue))) %>%
  arrange(-Nobs) %>%
  print_table_conditional
MEASURE Measure Nobs
CPI Consumer price index 2656065
IT_W Item weights 40409

ADJUSTMENT

Code 
PRICES_ALL %>%
  left_join(ADJUSTMENT, by = "ADJUSTMENT") %>%
  group_by(ADJUSTMENT, Adjustment) %>%
  summarise(Nobs = sum(!is.na(obsValue))) %>%
  arrange(-Nobs) %>%
  print_table_conditional
ADJUSTMENT Adjustment Nobs
N Neither seasonally adjusted nor calendar adjusted 2689798
S Seasonally adjusted, not calendar adjusted 6676

METHODOLOGY

Code 
PRICES_ALL %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  group_by(METHODOLOGY, Methodology) %>%
  summarise(Nobs = sum(!is.na(obsValue))) %>%
  arrange(-Nobs) %>%
  print_table_conditional
METHODOLOGY Methodology Nobs
N National 1924255
HICP Eurostat harmonised index of consumer prices (HICP) 772219

UNIT_MEASURE

Code 
PRICES_ALL %>%
  left_join(UNIT_MEASURE, by = "UNIT_MEASURE") %>%
  group_by(UNIT_MEASURE, Unit_measure) %>%
  summarise(Nobs = sum(!is.na(obsValue))) %>%
  arrange(-Nobs) %>%
  print_table_conditional
UNIT_MEASURE Unit_measure Nobs
IX Index 850316
PC Percentage change 837656
PA Percent per annum 821981
PD Percentage points 146112
10P3EXP_CNSMR Per 1 000 of consumer expenditure 40409

EXPENDITURE

Code 
PRICES_ALL %>%
  left_join(EXPENDITURE, by = "EXPENDITURE") %>%
  group_by(EXPENDITURE, Expenditure) %>%
  summarise(Nobs = sum(!is.na(obsValue))) %>%
  arrange(-Nobs) %>%
  print_table_conditional
EXPENDITURE Expenditure Nobs
_T Total 195607
CP01 Food and non-alcoholic beverages 164869
CP045_0722 Energy 145213
CP045 Electricity, gas and other fuels 114911
CP041 Actual rentals for housing 111984
_TXCP01_NRG All items non-food non-energy 109340
CP02 Alcoholic beverages, tobacco and narcotics 106883
CP04 Housing, water, electricity, gas and other fuels 106883
CP08 Communication 106883
CP06 Health 106628
CP03 Clothing and footwear 106298
CP05 Furnishings, household equipment and routine household maintenance 106298
CP09 Recreation and culture 106298
CP11 Restaurants and hotels 105849
CP0722 Fuels and lubricants for personal transport equipment 105682
CP07 Transport 105533
CP12 Miscellaneous goods and services 105509
CP10 Education 104137
SERV Services 100086
CP043 Maintenance and repair of the dwelling 96501
CP044 Water supply and miscellaneous services relating to the dwelling 95619
CP041T043X042 Housing excluding imputed rentals for housing 52874
GD Goods 51987
CP041T043 Housing 49138
_TXNRG_01_02 Overall index excluding energy, food, alcohol and tobacco 36221
SERVXCP041_042_0432 Services less housing 31587
CP042 Imputed rentals for housing 31381
SERVXCP041_0432 Services less housing (Housing excluding imputed rentals for housing) 29918
CPRES Residuals 6357

TRANSFORMATION

Code 
PRICES_ALL %>%
  left_join(TRANSFORMATION, by = "TRANSFORMATION") %>%
  group_by(TRANSFORMATION, Transformation) %>%
  summarise(Nobs = sum(!is.na(obsValue))) %>%
  arrange(-Nobs) %>%
  print_table_conditional
TRANSFORMATION Transformation Nobs
_Z Not applicable 890725
G1 Growth rate, period on period 837656
GY Growth rate, over 1 year 821981
GOY Contribution to growth rate, over 1 year 146112

REF_AREA

Code 
PRICES_ALL %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(REF_AREA, Ref_area) %>%
  summarise(Nobs = sum(!is.na(obsValue))) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Ref_area)),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

obsTime

Code 
PRICES_ALL %>%
  filter(!is.na(obsValue)) %>%
  group_by(obsTime) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(obsTime)) %>%
  print_table_conditional

Europe vs. US

1996-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("EA20", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_4flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

1996-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("EA20", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2002-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_4flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

2017-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("EA20", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2017-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_4flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

France vs. Germany

1996-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("FRA", "DEU"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_4flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

2017-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("FRA", "DEU"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2017-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_4flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

France vs. USA

1996-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("1996-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_4flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

2017-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2017-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_4flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))

2019-

Code 
PRICES_ALL %>%
  filter(EXPENDITURE == "_T",
         REF_AREA %in% c("FRA", "USA"),
         ADJUSTMENT == "N",
         MEASURE == "CPI",
         TRANSFORMATION == "_Z",
         FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(METHODOLOGY, by = "METHODOLOGY") %>%
  month_to_date() %>%
  filter(date >= as.Date("2019-01-01")) %>%
  group_by(Ref_area, Methodology) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Ref_area"  = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Methodology)) + 
  scale_color_identity() +
  scale_linetype_manual(values = c("dashed", "solid")) + add_4flags +
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  scale_y_log10(breaks = seq(10, 200, 5))