How big US economy is compared to others

Introduction

In a somehow crazy paper Washington Must Prepare for War With Both Russia and China (https://foreignpolicy.com/2022/02/18/us-russia-china-war-nato-quadrilateral-security-dialogue/) Matthew Kroenig (deputy director of the Atlantic Council’s Scowcroft Center for Strategy and Security.) claims that US is capable to fight on both fronts as ``The United States possesses 24 percent of global GDP compared to a combined 19 percent in China and Russia.’’

Lets check. The data are from the WorldBank

GDP total constant USD

As we compare global shares, current USD seems OK.

## Download from (and transformed to long format)
##https://data.worldbank.org/indicator/NY.GDP.MKTP.CD
gdp0 <- read.csv("GDP_total_p.csv", sep = ';',  header=T, na.string="NA")

gdp <- gdp0 %>% filter ( code %in% include ) %>%
  filter (year >= 1990) %>%
  select (code, gdp, year) %>%
  pivot_wider(names_from = code, values_from = gdp) %>%
  mutate(
    CHN = CHN/WLD * 100,
    EU = EUU/WLD * 100,
    IND = IND/WLD * 100, 
    RUS = RUS/WLD * 100,
    USA = USA/WLD * 100
  ) %>%
  select (year, CHN, EU, IND, RUS, USA) %>%
  pivot_longer(cols= c('CHN', 'EU', 'IND', 'RUS', 'USA'), 
               names_to = 'entity', values_to = 'gdp')

p1 <- ggplot(gdp, aes(x=year, y=gdp, color=entity )) +
  geom_point(size=.6, alpha=.3) +
  geom_smooth(method="loess", se=F, span=.5) +
  ylab(label="% global share") +
  xlab("") +
  scale_y_continuous(breaks = seq(0, 32, by=2)) +
  ggtitle("GDP", subtitle="source: World Bank") 
p1

## `geom_smooth()` using formula 'y ~ x'

seems Mr. Kroenig compared GDP in current/constant USD…

GDP/PPP total

PPP seems to be even more OK because of prices variations among countries.

## Download from (and transformed to long format)
## https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.CD
gdp0 <- read.csv("GDP_PPP_total_p.csv", sep = ';',  header=T, na.string="NA")

include <- c('CHN', 'EUU', 'IND', 'RUS', 'WLD', 'USA')

gdp <- gdp0 %>% filter ( code %in% include ) %>%
  filter (year >= 1990) %>%
  select (code, gdpppp, year) %>%
  pivot_wider(names_from = code, values_from = gdpppp) %>%
  mutate(
    CHN = CHN/WLD * 100,
     EU = EUU/WLD * 100,
    IND = IND/WLD * 100, 
    RUS = RUS/WLD * 100,
    USA = USA/WLD * 100
  ) %>%
  select (year, CHN, EU, IND, RUS, USA) %>%
  pivot_longer(cols= c('CHN', 'EU', 'IND', 'RUS', 'USA'), 
               names_to = 'entity', values_to = 'gdp')

p2 <- ggplot(gdp, aes(x=year, y=gdp, color=entity )) +
  geom_point(size=.6, alpha=.3) +
  geom_smooth(method="loess", se=F, span=.5) +
  ylab(label="% global share") +
  scale_y_continuous(breaks = seq(0, 24, by=2)) +
  xlab("") +
  ggtitle("GDP/PPP", subtitle="source: World Bank")
p2

## `geom_smooth()` using formula 'y ~ x'

In this version of GDP the US is not #1 any more…

Manufacturing

Service sector maybe not so crucial at war. Let’s check Manufacturing, value added (current US$) (https://data.worldbank.org/indicator/NV.IND.MANF.CD)

## https://data.worldbank.org/indicator/NV.IND.MANF.KN
mva0 <- read.csv("Manuf_ValueAdded_currUSD_p.csv", sep = ';',  header=T, na.string="NA")

mva <- mva0 %>% filter ( code %in% include ) %>%
  filter (year >= 1990) %>%
  select (code, mva, year) %>%
  pivot_wider(names_from = code, values_from = mva) %>%
  mutate(
    CHN = CHN/WLD * 100,
    EU = EUU/WLD * 100,
    IND = IND/WLD * 100,
    RUS = RUS/WLD * 100,
    USA = USA/WLD * 100
  ) %>%
  select (year, CHN, EU, IND, RUS, USA) %>%
  pivot_longer(cols= c('CHN', 'EU', 'IND', 'RUS', 'USA'),
               names_to = 'entity', values_to = 'mva')

p3 <- ggplot(mva, aes(x=year, y=mva, color=entity )) +
  geom_point(size=.6, alpha=.3) +
  geom_smooth(method="loess", se=F, span=.5) +
  ylab(label="% global share") +
  xlab("") +
  scale_y_continuous(breaks = seq(0, 32, by=2)) +
  ggtitle("Manufacturing Value Added/Constant USD", subtitle="source: World Bank")
p3

## `geom_smooth()` using formula 'y ~ x'

All in one chart

p12 <- ggarrange(p1, p2, p3, ncol = 3)

## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 48 rows containing non-finite values (stat_smooth).

## Warning: Removed 48 rows containing missing values (geom_point).

##ggsave(file="global_GDP_MVC.png", width=8)
p12