Economic Outlook No 113 - April 2023

Data - OECD

Author

François Geerolf

Info

source dataset Title .html .rData
oecd EO113_INTERNET Economic Outlook No 113 - April 2023 2026-08-11 2026-08-02
oecd EO Economic Outlook 2026-08-11 2026-08-11

LAST_DOWNLOAD

source dataset Title .html .rData
oecd EO Economic Outlook 2026-08-11 2026-08-11
oecd EO113_INTERNET Economic Outlook No 113 - April 2023 2026-08-11 2026-08-02
oecd EO112_INTERNET Economic Outlook No 112 - November 2022 2026-08-11 2026-08-02
oecd EO111_INTERNET Economic Outlook No 111 - June 2022 2026-08-11 2026-08-02
oecd EO110_INTERNET Economic Outlook No 110 - December 2021 2026-08-11 2026-08-02
oecd EO109_INTERNET Economic Outlook No 109 - May 2021 2026-08-11 2026-08-02
oecd EO108_INTERNET Economic Outlook No 108 - December 2020 2026-08-11 2026-08-02

LAST_COMPILE

LAST_COMPILE
2026-08-13

Last

obsTime Nobs
2025-Q4 1

VARIABLE

Code 
EO113_INTERNET |>
  left_join(EO113_INTERNET_var$VARIABLE, by = "VARIABLE") %>%
  mutate(Variable = Variable |> substr(1, 120)) |>
  group_by(VARIABLE, Variable) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

FREQUENCY

Code 
EO113_INTERNET |>
  left_join(EO113_INTERNET_var$FREQUENCY, by = "FREQUENCY") |>
  group_by(FREQUENCY, Frequency) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
FREQUENCY Frequency Nobs
Q Quarterly 781864
A Annual 399080

Vintages

Lithuania

Code 
EO110_INTERNET |>
  mutate(vintage = "Economic Outlook No 110 - December 2021") |>
  bind_rows(EO111_INTERNET |>
              mutate(vintage = "Economic Outlook No 111 - June 2022")) |>
  bind_rows(EO112_INTERNET |>
              mutate(vintage = "Economic Outlook No 112 - November 2022")) |>
  bind_rows(EO113_INTERNET |>
              mutate(vintage = "Economic Outlook No 113 - June 2023")) |>
  filter(VARIABLE == "GDPV", 
         LOCATION == "LTU",
         FREQUENCY == "Q") |>
  quarter_to_date() |>
  filter(date >= as.Date("2021-01-01")) |>
  group_by(vintage) |>
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2021-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = vintage)) +
  xlab("") + ylab("Gross Domestic Product in Volume, Lithuania") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))

Slovakia

Code 
EO110_INTERNET |>
  mutate(vintage = "Economic Outlook No 110 - December 2021") |>
  bind_rows(EO111_INTERNET |>
              mutate(vintage = "Economic Outlook No 111 - June 2022")) |>
  bind_rows(EO112_INTERNET |>
              mutate(vintage = "Economic Outlook No 112 - November 2022")) |>
  bind_rows(EO113_INTERNET |>
              mutate(vintage = "Economic Outlook No 113 - June 2023")) |>
  filter(VARIABLE == "GDPV", 
         LOCATION == "SVK",
         FREQUENCY == "Q") |>
  quarter_to_date() |>
  filter(date >= as.Date("2021-01-01")) |>
  group_by(vintage) |>
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2021-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = vintage)) +
  xlab("") + ylab("Gross Domestic Product in Volume, Slovakia") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))

Finland

Code 
EO110_INTERNET |>
  mutate(vintage = "Economic Outlook No 110 - December 2021") |>
  bind_rows(EO111_INTERNET |>
              mutate(vintage = "Economic Outlook No 111 - June 2022")) |>
  bind_rows(EO112_INTERNET |>
              mutate(vintage = "Economic Outlook No 112 - November 2022")) |>
  bind_rows(EO113_INTERNET |>
              mutate(vintage = "Economic Outlook No 113 - June 2023")) |>
  filter(VARIABLE == "GDPV", 
         LOCATION == "FIN",
         FREQUENCY == "Q") |>
  quarter_to_date() |>
  filter(date >= as.Date("2021-01-01")) |>
  group_by(vintage) |>
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2021-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = vintage)) +
  xlab("") + ylab("Gross Domestic Product in Volume, Finland") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))

Germany - Vintages

SubList

Code 
EO113_INTERNET |>
              mutate(vintage = "EO No 113 - June 2023") |>
  bind_rows(EO110_INTERNET |>
              mutate(vintage = "EO No 110 - December 2021")) |>
  filter(LOCATION == "DEU",
         obsTime == "2023-Q1",
         VARIABLE %in% c("GDPV", "ITV", "CPV", "CGV", "MGSV", "XGSV"),
         FREQUENCY == "Q") |>
  left_join(EO113_INTERNET_var$VARIABLE, by = "VARIABLE") |>
  select(VARIABLE, Variable, UNIT, vintage, obsValue) |>
  spread(vintage, obsValue) |>
  mutate(`Change (%)` = round(100*(`EO No 113 - June 2023`/`EO No 110 - December 2021`-1), 2)) |>
  print_table_conditional()
VARIABLE Variable UNIT EO No 110 - December 2021 EO No 113 - June 2023 Change (%)
CGV Government final consumption expenditure, volume EUR 7.079714e+11 6.749840e+11 -4.66
CPV Private final consumption expenditure, volume EUR 1.764611e+12 1.673660e+12 -5.15
GDPV Gross domestic product, volume, market prices EUR 3.360288e+12 3.238844e+12 -3.61
ITV Gross fixed capital formation, total, volume EUR 7.141888e+11 6.806400e+11 -4.70
MGSV Imports of goods and services, volume (national accounts basis) EUR 1.476323e+12 1.472700e+12 -0.25
XGSV Exports of goods and services, volume (national accounts basis) EUR 1.634885e+12 1.623416e+12 -0.70

List

Code 
EO113_INTERNET |>
              mutate(vintage = "EO No 113 - June 2023") |>
  bind_rows(EO110_INTERNET |>
              mutate(vintage = "EO No 110 - December 2021")) |>
  filter(LOCATION == "DEU",
         obsTime == "2023-Q1",
         FREQUENCY == "Q") |>
  left_join(EO113_INTERNET_var$VARIABLE, by = "VARIABLE") |>
  select(VARIABLE, Variable, UNIT, vintage, obsValue) |>
  spread(vintage, obsValue) |>
  mutate(`Change (%)` = round(100*(`EO No 113 - June 2023`/`EO No 110 - December 2021`-1), 2)) |>
  print_table_conditional()

GDP in Volume

Since 2014Q1

Code 
EO110_INTERNET |>
  mutate(vintage = "Economic Outlook No 110 - December 2021") |>
  bind_rows(EO111_INTERNET |>
              mutate(vintage = "Economic Outlook No 111 - June 2022")) |>
  bind_rows(EO112_INTERNET |>
              mutate(vintage = "Economic Outlook No 112 - November 2022")) |>
  bind_rows(EO113_INTERNET |>
              mutate(vintage = "Economic Outlook No 113 - June 2023")) |>
  filter(VARIABLE == "GDPV", 
         LOCATION == "DEU",
         FREQUENCY == "Q") |>
  quarter_to_date() |>
  filter(date >= as.Date("2014-01-01")) |>
  group_by(vintage) |>
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2014-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = vintage)) +
  xlab("") + ylab("Gross Domestic Product in Volume, Germany") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))

Since 2017Q1

Code 
EO110_INTERNET |>
  mutate(vintage = "Economic Outlook No 110 - December 2021") |>
  bind_rows(EO111_INTERNET |>
              mutate(vintage = "Economic Outlook No 111 - June 2022")) |>
  bind_rows(EO112_INTERNET |>
              mutate(vintage = "Economic Outlook No 112 - November 2022")) |>
  bind_rows(EO113_INTERNET |>
              mutate(vintage = "Economic Outlook No 113 - June 2023")) |>
  filter(VARIABLE == "GDPV", 
         LOCATION == "DEU",
         FREQUENCY == "Q") |>
  quarter_to_date() |>
  filter(date >= as.Date("2017-01-01")) |>
  group_by(vintage) |>
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2017-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = vintage)) +
  xlab("") + ylab("Gross Domestic Product in Volume, Germany") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))

Since 2021

Code 
EO110_INTERNET |>
  mutate(vintage = "Economic Outlook No 110 - December 2021") |>
  bind_rows(EO111_INTERNET |>
              mutate(vintage = "Economic Outlook No 111 - June 2022")) |>
  bind_rows(EO112_INTERNET |>
              mutate(vintage = "Economic Outlook No 112 - November 2022")) |>
  bind_rows(EO113_INTERNET |>
              mutate(vintage = "Economic Outlook No 113 - June 2023")) |>
  filter(VARIABLE == "GDPV", 
         LOCATION == "DEU",
         FREQUENCY == "Q") |>
  quarter_to_date() |>
  filter(date >= as.Date("2021-01-01")) |>
  group_by(vintage) |>
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2021-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = vintage)) +
  xlab("") + ylab("Gross Domestic Product in Volume, Germany") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))

Consumption in Volume

Code 
EO110_INTERNET |>
  mutate(vintage = "Economic Outlook No 110 - December 2021") |>
  bind_rows(EO111_INTERNET |>
              mutate(vintage = "Economic Outlook No 111 - June 2022")) |>
  bind_rows(EO112_INTERNET |>
              mutate(vintage = "Economic Outlook No 112 - November 2022")) |>
  bind_rows(EO113_INTERNET |>
              mutate(vintage = "Economic Outlook No 113 - June 2023")) |>
  filter(VARIABLE == "CPV", 
         LOCATION == "DEU",
         FREQUENCY == "Q") |>
  quarter_to_date() |>
  filter(date >= as.Date("2021-01-01")) |>
  group_by(vintage) |>
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2021-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = vintage)) +
  xlab("") + ylab("Consumption in Volume, Germany") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))

Consumption in Volume

Code 
EO110_INTERNET |>
  mutate(vintage = "Economic Outlook No 110 - December 2021") |>
  bind_rows(EO111_INTERNET |>
              mutate(vintage = "Economic Outlook No 111 - June 2022")) |>
  bind_rows(EO112_INTERNET |>
              mutate(vintage = "Economic Outlook No 112 - November 2022")) |>
  bind_rows(EO113_INTERNET |>
              mutate(vintage = "Economic Outlook No 113 - June 2023")) |>
  filter(VARIABLE == "ITV", 
         LOCATION == "DEU",
         FREQUENCY == "Q") |>
  quarter_to_date() |>
  filter(date >= as.Date("2021-01-01")) |>
  group_by(vintage) |>
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("2021-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = vintage)) +
  xlab("") + ylab("Investment in Volume, Germany") + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(20, 200, 1))

GDP in Volume

Growth revisions by country for 2023-Q1

Table

Code 
EO110_INTERNET |>
  mutate(vintage = "2021-12") |>
  bind_rows(EO111_INTERNET |>
              mutate(vintage = "2022-06")) |>
  bind_rows(EO112_INTERNET |>
              mutate(vintage = "2022-11")) |>
  bind_rows(EO113_INTERNET |>
              mutate(vintage = "2023-06")) |>
  left_join(EO113_INTERNET_var$LOCATION, by = "LOCATION") |>
  filter(VARIABLE == "GDPV", 
         obsTime == "2023-Q1",
         FREQUENCY == "Q") |>
  select(vintage, Location, obsValue) |>
  group_by(Location) |>
  mutate(obsValue = 100*obsValue/obsValue[vintage == "2021-12"]) |>
  spread(vintage, obsValue) |>
  arrange(`2023-06`) |>
  print_table_conditional()

Output Gap (% of GDP)

Germany, Spain, Greece, Italy

Code 
EO113_INTERNET |>
  filter(FREQUENCY == "A",
         VARIABLE == "GAP",
         LOCATION %in% c("DEU", "ESP", "GRC", "ITA")) |>
  left_join(EO113_INTERNET_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  select(Location, date, obsValue) |>
  arrange(Location, date) |>
  mutate(obsValue = obsValue/100) |>
  left_join(colors, by = c("Location" = "country")) |>
  ggplot() + 
  geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Output Gap (% of GDP)") + xlab("")

United States, France, Germany, Switzerland

Code 
EO113_INTERNET |>
  filter(FREQUENCY == "A",
         VARIABLE == "GAP",
         LOCATION %in% c("CHE", "FRA", "DEU", "USA")) |>
  left_join(EO113_INTERNET_var$LOCATION, by = "LOCATION") |>
  year_to_date() |>
  select(Location, date, obsValue) |>
  arrange(Location, date) |>
  mutate(obsValue = obsValue/100) |>
  left_join(colors, by = c("Location" = "country")) |>
  ggplot() + 
  geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Output Gap (% of GDP)") + xlab("")

Unemployment Rate

Germany, Spain, Greece, Italy

All

Code 
EO113_INTERNET |>
  filter(FREQUENCY == "Q",
         VARIABLE == "UNR",
         LOCATION %in% c("DEU", "ITA", "FRA", "USA")) |>
  left_join(EO113_INTERNET_var$LOCATION, by = "LOCATION") |>
  quarter_to_date() |>
  select(Location, date, obsValue) |>
  arrange(Location, date) |>
  mutate(obsValue = obsValue/100) |>
  left_join(colors, by = c("Location" = "country")) |>
  ggplot() + 
  geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Unemployment Rate (%)") + xlab("")

1990-

Code 
EO113_INTERNET |>
  filter(FREQUENCY == "Q",
         VARIABLE == "UNR",
         LOCATION %in% c("DEU", "ITA", "FRA", "USA")) |>
  left_join(EO113_INTERNET_var$LOCATION, by = "LOCATION") |>
  quarter_to_date() |>
  filter(date >= as.Date("1990-01-01")) |>
  select(Location, date, obsValue) |>
  arrange(Location, date) |>
  mutate(obsValue = obsValue/100) |>
  left_join(colors, by = c("Location" = "country")) |>
  ggplot() + 
  geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Unemployment Rate (%)") + xlab("")

2010-

Code 
EO113_INTERNET |>
  filter(FREQUENCY == "Q",
         VARIABLE == "UNR",
         LOCATION %in% c("DEU", "ITA", "FRA", "USA")) |>
  left_join(EO113_INTERNET_var$LOCATION, by = "LOCATION") |>
  quarter_to_date() |>
  filter(date >= as.Date("2010-01-01")) |>
  select(Location, date, obsValue) |>
  arrange(Location, date) |>
  mutate(obsValue = obsValue/100) |>
  left_join(colors, by = c("Location" = "country")) |>
  ggplot() + 
  geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + scale_color_identity() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Unemployment Rate (%)") + xlab("")