GDP, PPP (current international D)

Data - WDI

Author

François Geerolf

Info

source dataset .html .RData

wdi

NY.GDP.MKTP.PP.CD

 2024-09-15 2024-09-18

Data on macro

source dataset .html .RData

eurostat

nama_10_a10

 2024-09-15 2024-09-15

eurostat

nama_10_a10_e

 2024-09-15 2024-09-18

eurostat

nama_10_gdp

 2024-09-15 2024-09-15

eurostat

nama_10_lp_ulc

 2024-09-15 2024-09-15

eurostat

namq_10_a10

 2024-09-15 2024-09-18

eurostat

namq_10_a10_e

 2024-09-15 2024-09-15

eurostat

namq_10_gdp

 2024-09-04 2024-09-15

eurostat

namq_10_lp_ulc

 2024-09-15 2024-09-15

eurostat

namq_10_pc

 2024-08-21 2024-09-15

eurostat

nasa_10_nf_tr

 2024-09-15 2024-09-15

eurostat

nasq_10_nf_tr

 2024-09-02 2024-09-02

fred

gdp

 2024-08-29 2024-09-18

oecd

QNA

 2024-06-06 2024-06-30

oecd

SNA_TABLE1

 2024-09-15 2024-06-30

oecd

SNA_TABLE14A

 2024-09-15 2024-06-30

oecd

SNA_TABLE2

 2024-07-01 2024-04-11

oecd

SNA_TABLE6A

 2024-07-01 2024-06-30

wdi

NE.RSB.GNFS.ZS

 2024-09-18 2024-09-18

wdi

NY.GDP.MKTP.CD

 2024-09-18 2024-09-18

wdi

NY.GDP.MKTP.PP.CD

 2024-09-15 2024-09-18

wdi

NY.GDP.PCAP.CD

 2024-09-15 2024-09-18

wdi

NY.GDP.PCAP.KD

 2024-09-15 2024-09-18

wdi

NY.GDP.PCAP.PP.CD

 2024-09-15 2024-09-18

wdi

NY.GDP.PCAP.PP.KD

 2024-09-15 2024-09-18

LAST_COMPILE

LAST_COMPILE
2024-09-18

Nobs - Javascript

Code 
NY.GDP.MKTP.PP.CD %>%
  left_join(iso2c, by = "iso2c") %>%
  group_by(iso2c, Iso2c) %>%
  
  mutate(value = round(value/(10^9))) %>%
  summarise(Nobs = n(),
            `Year 1` = first(year),
            `GDP 1 (Bn)` = first(value) %>% paste0("$ ", .),
            `Year 2` = last(year),
            `GDP 2 (Bn)` = last(value) %>% paste0("$ ", .)) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Iso2c)),
         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 .}

2018 GDP by Country

Code 
NY.GDP.MKTP.PP.CD %>%
  right_join(iso2c %>%
              filter((region != "Aggregates") & (region != "NA")), 
             by = "iso2c") %>%
  group_by(iso2c, Iso2c) %>%
  
  mutate(value = round(value/(10^9))) %>%
  summarise(`GDP (Bn)` = last(value)) %>%
  arrange(-`GDP (Bn)`) %>%
  mutate(`GDP (Bn)` = `GDP (Bn)` %>% paste0("$ ", ., " Bn")) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Iso2c)),
         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 .}

2018 GDP by Country and Aggregates

Code 
NY.GDP.MKTP.PP.CD %>%
  right_join(iso2c, by = "iso2c") %>%
  group_by(iso2c, Iso2c) %>%
  
  mutate(value = round(value/(10^9))) %>%
  summarise(`GDP (Bn)` = last(value)) %>%
  arrange(-`GDP (Bn)`) %>%
  mutate(`GDP (Bn)` = `GDP (Bn)` %>% paste0("$ ", ., " Bn")) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Euro Area vs. US

Base 100

Code 
NY.GDP.MKTP.PP.CD %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  filter(iso2c %in% c("XC", "US"),
         date >= as.Date("2008-01-01")) %>%
  group_by(iso2c) %>%
  arrange(date) %>%
  mutate(value = 100*value/value[1]) %>%
  mutate(Iso2c = ifelse(iso2c == "XC", "Europe", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_2flags +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(70, 300, 5)) + 
  xlab("") + ylab("PIB en $ (100 = 2008)")

Avec dollars

Code 
NY.GDP.MKTP.PP.CD %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  filter(iso2c %in% c("XC", "US"),
         date >= as.Date("2008-01-01")) %>%
  group_by(iso2c) %>%
  arrange(date) %>%
  mutate(Iso2c = ifelse(iso2c == "XC", "Europe", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "US", color2, color)) %>%
  ungroup %>%
  mutate(dollar = value/10^9,
         value = 100*value/value[2]) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_2flags +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 300, 5)) + 
  xlab("") + ylab("PIB en $ (100 = Zone Euro, 2008)") + 
  geom_text_repel(data = . %>% filter(year(date) %in% seq(2008, 2022, 2)),
                                      aes(x = date, y = value, label = paste0("$", round(dollar, digits = -2), " Md")))

China, E.U., U.S.

Linear

Code 
NY.GDP.MKTP.PP.CD %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "US", "CN", "EU")) %>%
  group_by(year) %>%
  mutate(value = value/value[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "EU", "Europe", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 2),
                     labels = scales::percent_format(accuracy = 1),
                     limits = c(0, 0.23)) + 
  xlab("") + ylab("% du PIB mondial en Parité de Pouvoir d'Achat")

Log

Code 
NY.GDP.MKTP.PP.CD %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "US", "CN", "EU")) %>%
  group_by(year) %>%
  mutate(value = value/value[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "EU", "Europe", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = 0.01*seq(0, 70, 5),
                     labels = scales::percent_format(accuracy = 1)) + 
  xlab("") + ylab("% du PIB mondial en Parité de Pouvoir d'Achat")

PPP vs current

EU27

Code 
plot1 <- NY.GDP.MKTP.CD %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "US", "CN", "EU")) %>%
  group_by(year) %>%
  filter(n() > 3) %>%
  mutate(value = value/value[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "XC", "Europe", Iso2c)) %>%
  filter(date >= as.Date("1990-01-01")) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c != "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  theme(legend.title = element_blank(),
        legend.position = c(0.85, 0.85)) +
  scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 5),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 35)) + 
  xlab("") + ylab("% du PIB mondial") +
  ggtitle("En $ Courants")

plot2 <- NY.GDP.MKTP.PP.CD %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "US", "CN", "EU")) %>%
  group_by(year) %>%
  filter(n() > 3) %>%
  mutate(value = value/value[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "XC", "Europe", Iso2c)) %>%
  filter(date >= as.Date("1990-01-01")) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c != "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  theme(legend.title = element_blank(),
        legend.position = c(0.85, 0.85)) +
  scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 5),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 35)) + 
  xlab("") + ylab("% du PIB mondial") +
  ggtitle("En $ Parités de Pouvoir d'Achat")

ggpubr::ggarrange(plot1, plot2)

Eurozone

Code 
plot1 <- NY.GDP.MKTP.CD %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "US", "CN", "XC")) %>%
  group_by(year) %>%
  filter(n() > 3) %>%
  mutate(value = value/value[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "XC", "Europe", Iso2c)) %>%
  filter(date >= as.Date("1990-01-01")) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c != "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  theme(legend.title = element_blank(),
        legend.position = c(0.85, 0.85)) +
  scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 5),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 35)) + 
  xlab("") + ylab("% du PIB mondial") +
  ggtitle("En $ Courants")

plot2 <- NY.GDP.MKTP.PP.CD %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "US", "CN", "XC")) %>%
  group_by(year) %>%
  filter(n() > 3) %>%
  mutate(value = value/value[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "XC", "Europe", Iso2c)) %>%
  filter(date >= as.Date("1990-01-01")) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c != "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  theme(legend.title = element_blank(),
        legend.position = c(0.85, 0.85)) +
  scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 5),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 35)) + 
  xlab("") + ylab("% du PIB mondial") +
  ggtitle("En $ Parités de Pouvoir d'Achat")

ggpubr::ggarrange(plot1, plot2)

Italy, France, Allemagne, Espagne

Linear

Code 
NY.GDP.MKTP.PP.CD %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "IT", "FR", "DE", "ES")) %>%
  group_by(year) %>%
  mutate(value = value/value[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "EU", "Europe", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_4flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, .5),
                     labels = scales::percent_format(accuracy = .1)) + 
  xlab("") + ylab("% du PIB mondial en Parité de Pouvoir d'Achat")

Log

Code 
NY.GDP.MKTP.PP.CD %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "IT", "FR", "DE", "ES")) %>%
  group_by(year) %>%
  mutate(value = value/value[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "EU", "Europe", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_4flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = 0.01*seq(0, 70, .5),
                     labels = scales::percent_format(accuracy = .1)) + 
  xlab("") + ylab("% du PIB mondial en Parité de Pouvoir d'Achat")

South Korea, Russia, Indonesia

Code 
NY.GDP.MKTP.PP.CD %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "ID", "KR", "RU")) %>%
  group_by(year) %>%
  mutate(value = value/value[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "EU", "Europe", Iso2c)) %>%
  mutate(Iso2c = ifelse(iso2c == "KR", "Korea", Iso2c)) %>%
  mutate(Iso2c = ifelse(iso2c == "RU", "Russia", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 0.5),
                     labels = scales::percent_format(accuracy = .1)) + 
  xlab("") + ylab("% du World GDP")

Japan, India, United Kingdom

Code 
NY.GDP.MKTP.PP.CD %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "IN", "JP", "GB")) %>%
  group_by(year) %>%
  mutate(value = value/value[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "EU", "Europe", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 2),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 10)) + 
  xlab("") + ylab("% of World GDP")

Germany, France, Italy

Code 
NY.GDP.MKTP.PP.CD %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "DE", "FR", "IT")) %>%
  group_by(year) %>%
  mutate(value = value/value[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "EU", "Europe", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 2),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 6)) + 
  xlab("") + ylab("% of World GDP")

Eurozone, European Union, United States

Code 
NY.GDP.MKTP.PP.CD %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "XC", "US", "EU")) %>%
  group_by(year) %>%
  mutate(value = value/value[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "EU", "Europe", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 5),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 25)) + 
  xlab("") + ylab("% of World GDP")

Germany, Japan, United Kingdom

Code 
NY.GDP.MKTP.PP.CD %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "JP", "DE", "GB")) %>%
  group_by(year) %>%
  mutate(value = value/value[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "EU", "Europe", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 2),
                     labels = scales::percent_format(accuracy = 1),
                     limits = 0.01*c(0, 10)) + 
  xlab("") + ylab("% of World GDP")

United Kingdom, France, Italy

Code 
NY.GDP.MKTP.PP.CD %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "GB", "FR", "IT")) %>%
  group_by(year) %>%
  mutate(value = value/value[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "EU", "Europe", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 0.5),
                     labels = scales::percent_format(accuracy = .1)) + 
  xlab("") + ylab("% of World GDP")

Brazil, Russia, India

Code 
NY.GDP.MKTP.PP.CD %>%
  right_join(iso2c, by = "iso2c") %>%
  filter(iso2c %in% c("1W", "BR", "RU", "IN")) %>%
  mutate(Iso2c = ifelse(iso2c == "RU", "Russia", Iso2c)) %>%
  group_by(year) %>%
  mutate(value = value/value[iso2c == "1W"]) %>%
  year_to_date %>%
  filter(!(iso2c == "1W")) %>%
  mutate(Iso2c = ifelse(iso2c == "EU", "Europe", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_3flags +
  scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 70, 1),
                     labels = scales::percent_format(accuracy = 1)) + 
  xlab("") + ylab("% of World GDP")