Economic Outlook No 113 - April 2023

Data - OECD

~/data/oecd/

Info

source	dataset	.html	.RData
oecd	EO	2024-04-16	2024-04-15
oecd	EO113_INTERNET	2024-04-15	2023-06-07

LAST_DOWNLOAD

source	dataset	.html	.RData
oecd	EO	2024-04-16	2024-04-15
oecd	EO108_INTERNET	2024-04-16	2020-12-05
oecd	EO109_INTERNET	2024-04-16	2023-03-18
oecd	EO110_INTERNET	2024-04-16	2023-03-18
oecd	EO111_INTERNET	2024-04-16	2023-03-18
oecd	EO112_INTERNET	2024-04-16	2023-03-18
oecd	EO113_INTERNET	2024-04-15	2023-06-07

LAST_COMPILE

LAST_COMPILE
2024-04-16

Last

obsTime	Nobs
2025-Q4	1

VARIABLE

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

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

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, 2025, 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

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, 2025, 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

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, 2025, 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

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

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

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, 2025, 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

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, 2025, 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

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, 2025, 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

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, 2025, 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

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, 2025, 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

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

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, 2025, 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

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, 2025, 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

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, 2025, 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-

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, 2025, 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-

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, 2025, 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("")