CGV
JavierPi
2024-02-20
Drivers of the crisis of the hyper-globalization: National trajectories and disputes between the Global North and South.
A Clustering Approach
Abstract:
This article explores how the unequal and paradoxical distribution of the benefits of hyper-globalization has fostered its own crisis. By analyzing the national trajectories of Global North and Global South countries within Global Value Chains (GVCs), we use K-means clustering to identify distinct patterns of international trade and economic growth over time. While some countries, mainly from the Global South, have successfully used GVCs for growth, others, mainly from the Global North, have experienced stagnation or decline. These differences highlight the complexity of the effects of hyper-globalization and explain the need to promote transformations for those countries that have not fully benefited from this system. These findings have important implications for understanding the economic and political dynamics of the new global order.
knitr::opts_chunk$set(echo = TRUE)
#install.packages(c("tidyverse","readxl", "wbstats","rempsyc", "ggrepel","ggtext", "scales","gghighlight","factoextra", "cluster"))
library(tidyverse)
library(readxl)
library(wbstats)
library(rempsyc)
library(ggrepel)
library(ggtext)
library(scales)
library(gghighlight)
library(factoextra)
library(cluster)
library(gridExtra)
options(scipen=999)url <- "https://www.worldmrio.com/unctadgvc/Database_GVC_2018update_rev0323.csv"
GVC_base <- read.csv(url)
divide_columnas <- function(x, y) {
return(x / y)
}
GVC<- GVC_base %>%
mutate(across(starts_with("GVC"),
~ divide_columnas(.x, get(paste0("VA_exp", gsub("\\D", "", cur_column())))),
.names = "{.col}_percent")) %>%
select("X" ,"GVC1990_percent","GVC1995_percent","GVC2000_percent","GVC2008_percent","GVC2018_percent") %>%
pivot_longer(names_to = "Year", cols = contains("_")) %>%
mutate(Year = str_replace(Year, "_percent", "")) %>%
mutate(Year = str_replace(Year, "GVC", "")) %>%
rename(country = X) %>%
rename(Insertión = value)Gdp_current <-"NY.GDP.MKTP.CD"
PBI<-wb_data(indicator= Gdp_current,start_date = 1990, end_date = 2018,country = "countries_only") %>%
pivot_wider(names_from = date,values_from = NY.GDP.MKTP.CD) %>%
filter( is.na(footnote)) %>%
mutate_if(~ any("iso3c" == "ISR"), list(`1990` = ~ PBI$`1995`))
PBI$"1990"[PBI$iso3c == "ISR"]<- 104892955202
PBI_per<-PBI
columnas_anio <- grep("^\\d{4}$", names(PBI_per))
for (i in columnas_anio) {
nombre_columna <- names(PBI_per)[i]
PBI_per[[paste0(nombre_columna, "%")]] <- (PBI_per[[nombre_columna]] / PBI_per$`1990`) * 100
}
PBI_per <- PBI_per %>%
select(iso3c, country, "1990%","1995%","2000%","2008%","2018%" ) %>%
pivot_longer(names_to = "Year",cols = contains("%")) %>%
mutate(Year = str_replace(Year, "%", ""))
total_2018<- sum(PBI$"2018", na.rm = TRUE)
PBI_list <- PBI %>%
select(iso3c, country, `2018`) %>%
mutate(percentage = `2018` / total_2018 * 100) %>%
arrange(desc(percentage)) %>%
mutate(NG_SG = case_when(
iso3c %in% c("DZA", "ARG", "BGD", "BRA", "CHL", "CHN", "COL", "CZE", "EGY", "HKG", "HUN", "IND", "IDN", "IRN", "IRQ", "KAZ", "MYS", "MEX", "NGA", "PAK", "PER", "PHL", "POL", "ROU", "RUS", "SAU", "SGP", "ZAF", "KOR", "THA", "TUR", "VEN", "VNM", "KWT", "QAT", "ARE") ~ "GS",
iso3c %in% c("AUS", "AUT", "BEL", "CAN", "DNK", "FIN", "FRA", "DEU", "GRC", "IRL", "ISR", "ITA", "JPN", "NLD", "NZL", "NOR", "PRT", "ESP", "SWE", "CHE", "GBR", "UKR", "USA", "LUX", "ISL", "MCO") ~ "GN",
TRUE ~ "GS")) %>%
mutate(X = case_when(
iso3c == "DZA" ~ "Algeria",
iso3c == "ARG" ~ "Argentina",
iso3c == "AUS" ~ "Australia",
iso3c == "AUT" ~ "Austria",
iso3c == "BGD" ~ "Bangladesh",
iso3c == "BEL" ~ "Belgium",
iso3c == "BRA" ~ "Brazil",
iso3c == "CAN" ~ "Canada",
iso3c == "CHL" ~ "Chile",
iso3c == "CHN" ~ "China",
iso3c == "COL" ~ "Colombia",
iso3c == "CZE" ~ "Czech Republic",
iso3c == "DNK" ~ "Denmark",
iso3c == "EGY" ~ "Egypt",
iso3c == "FIN" ~ "Finland",
iso3c == "FRA" ~ "France",
iso3c == "DEU" ~ "Germany",
iso3c == "GRC" ~ "Greece",
iso3c == "HKG" ~ "Hong Kong",
iso3c == "HUN" ~ "Hungary",
iso3c == "IND" ~ "India",
iso3c == "IDN" ~ "Indonesia",
iso3c == "IRN" ~ "Iran",
iso3c == "IRQ" ~ "Iraq",
iso3c == "IRL" ~ "Ireland",
iso3c == "ISR" ~ "Israel",
iso3c == "ITA" ~ "Italy",
iso3c == "JPN" ~ "Japan",
iso3c == "KAZ" ~ "Kazakhstan",
iso3c == "MYS" ~ "Malaysia",
iso3c == "MEX" ~ "Mexico",
iso3c == "NLD" ~ "Netherlands",
iso3c == "NZL" ~ "New Zealand",
iso3c == "NGA" ~ "Nigeria",
iso3c == "NOR" ~ "Norway",
iso3c == "PAK" ~ "Pakistan",
iso3c == "PER" ~ "Peru",
iso3c == "PHL" ~ "Philippines",
iso3c == "POL" ~ "Poland",
iso3c == "PRT" ~ "Portugal",
iso3c == "ROU" ~ "Romania",
iso3c == "RUS" ~ "Russia",
iso3c == "SAU" ~ "Saudi Arabia",
iso3c == "SGP" ~ "Singapore",
iso3c == "ZAF" ~ "South Africa",
iso3c == "KOR" ~ "South Korea",
iso3c == "ESP" ~ "Spain",
iso3c == "SWE" ~ "Sweden",
iso3c == "CHE" ~ "Switzerland",
iso3c == "THA" ~ "Thailand",
iso3c == "TUR" ~ "Turkey",
iso3c == "GBR" ~ "UK",
iso3c == "UKR" ~ "Ukraine",
iso3c == "USA" ~ "USA",
iso3c == "VEN" ~ "Venezuela",
iso3c == "VNM" ~ "Viet Nam",
iso3c == "KWT" ~ "Kuwait",
iso3c == "QAT" ~ "Qatar",
iso3c == "ARE"~ "UAE",
))
list<-PBI_list %>%
slice_head(n = 48) %>%
select(iso3c,country,NG_SG,X) %>%
rename(country_s = country) %>%
rename (country = X)PBI_CGV<- list %>%
left_join(GVC, by = "country")
PBI_CGV<- PBI_CGV %>%
left_join(PBI_per, by = c("iso3c", "Year")) %>%
select(!contains(".")) %>%
rename(Country = country_s,
PBI_Var = value)
intercepts<- PBI_CGV %>%
filter(Year == 2018) %>%
summarise(median_PBI_Var = median(PBI_Var, na.rm = TRUE),
median_Insertión = median(Insertión, na.rm = TRUE))
classification <- PBI_CGV %>%
filter(Year == 2018) %>%
mutate(cuadrant = case_when (
Insertión < intercepts$median_Insertión & PBI_Var > intercepts$median_PBI_Var ~ "High Growth without Insertion",
Insertión > intercepts$median_Insertión & PBI_Var > intercepts$median_PBI_Var ~ "High Growth with Insertion",
Insertión < intercepts$median_Insertión & PBI_Var < intercepts$median_PBI_Var ~ "Low Growth without Insertion",
Insertión > intercepts$median_Insertión & PBI_Var < intercepts$median_PBI_Var ~ "Low Growth with Insertion"))%>%
select(iso3c,cuadrant) %>%
mutate(cuadrant = factor(cuadrant, levels = c("High Growth without Insertion","High Growth with Insertion","Low Growth without Insertion","Low Growth with Insertion")))
PBI_CGV<-PBI_CGV %>%
left_join(classification, by = "iso3c" ) FVA_per<-GVC_base %>%
summarise(across(starts_with("FVA") | starts_with("VA_"), sum)) %>%
select(starts_with("FVA")) %>%
pivot_longer(everything(), values_to = "FVA") %>%
separate(col = "name",into = c("Algo", "Año"),sep = 3 ) %>%
select(2:3)
VA_per<-GVC_base %>%
summarise(across(starts_with("FVA") | starts_with("VA_"), sum)) %>%
select(starts_with("VA_")) %>%
pivot_longer(everything(), values_to = "VA_") %>%
separate(col = "name",into = c("Algo", "Año"),sep = 6 ) %>%
select(2:3)
FVA_def <- FVA_per %>%
left_join(VA_per, by = "Año") %>%
mutate(FVA_per = round(FVA/VA_ * 100, 1)) %>%
select(c("Año", "FVA_per")) %>%
mutate(Año = as.numeric(Año))
rm("FVA_per","VA_per")
Gdp_current <-"NY.GDP.MKTP.CD"
PBI_2<-wb_data(indicator= Gdp_current,start_date = 1990, end_date = 2018,country = "all") %>%
filter(iso3c == "WLD") %>%
select(c("date","NY.GDP.MKTP.CD")) %>%
rename("Año" = "date",
"PBI" = "NY.GDP.MKTP.CD")
PBI_FVA<- PBI_2 %>%
left_join(FVA_def, by = "Año" )Figure_1 <- ggplot(PBI_FVA, aes(x = Año)) +
geom_bar(aes(y = PBI/1000000000), stat = "identity", fill = "#BDBDBD") +
geom_line(aes(y = FVA_per* 1900, group = 3), color = "#616161", lwd = 1.3, linetype = 7) +
geom_text(aes(y = FVA_per * 1900 + 2000, label = paste0(round(FVA_per, 2), "%")), size = 3, fontface = "bold") +
geom_text(aes(y = PBI/2000000000, label = paste0(round(PBI/1000000000000, 0))), size = 5, color = "white",fontface = "bold") +
scale_y_continuous(sec.axis = sec_axis(~./1900, name = "FVA in %")) +
scale_x_continuous(breaks =c(1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018)) +
theme_minimal() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.y.right = element_blank(),
axis.title.y.left = element_blank(),
axis.text.y.right =element_blank(),
axis.text.y.left = element_blank(),
axis.text.x = element_text(size = 10, color = "black"),
axis.title.x = element_blank())
Figure_1
PBI_NGSG <- PBI %>%
left_join(PBI_list, by = "iso3c") %>%
rename("2018" = "2018.x") %>%
select(iso3c, NG_SG, c(`1990`:`2018`)) %>%
pivot_longer(cols = c(`1990`:`2018`),
names_to = "year",
values_to = "GDP") %>%
mutate(year = as.numeric(year)) %>%
group_by(NG_SG, year) %>%
summarise(GDP = sum(GDP, na.rm = TRUE))
Figure_2 <- PBI_NGSG %>%
ggplot(aes(x = year, y = GDP/1000000000000, fill = NG_SG)) +
geom_area(position = "jitter") +
geom_line(data = PBI_NGSG %>% filter(NG_SG == "GS"), aes(group = 1), lwd = 1.5, color = "white", lineend = "round", linejoin = "round")+
scale_y_continuous(labels = label_number(suffix = "B")) +
scale_x_continuous(breaks = c(1990:2018)) +
scale_fill_manual(values = c("#616161", "#BDBDBD")) +
theme_minimal() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.title = element_blank(),
legend.position = c(.10,.90))
Figure_2
# Figure_3.1 <- ggplot(PBI_CGV %>% arrange(Year)) +
# geom_ribbon(aes(x = Insertión, y = PBI_Var, ymin = min(PBI_Var), ymax = intercepts$median_PBI_Var, xmax =max(Insertión)),fill = "#CCCCCC", alpha = 0.5) +
# geom_ribbon(aes(x = Insertión, y = PBI_Var, xmin = intercepts$median_Insertión, xmax = max(Insertión)),fill ="#CCCCCC", alpha = 0.5)+
# geom_path(aes(x = Insertión, y = PBI_Var, group = iso3c, color = NG_SG),
# arrow = arrow(length = unit(0.10, "inches")), lwd = 1.8, alpha = 1,
# lineend = "round", linejoin = "round") +
# geom_path(aes(x = Insertión, y = PBI_Var, group = iso3c),
# arrow = arrow(length = unit(0.10, "inches")), lwd = 0.5, alpha = 0.5,
# lineend = "round", linejoin = "round", color ="black") +
# geom_text_repel(data = PBI_CGV %>% filter(Year == 2018), aes(x = Insertión, y = PBI_Var, label = iso3c), size = 3, color = "black", fontface='bold') +
# geom_vline(xintercept = intercepts$median_Insertión) +
# geom_hline(yintercept = intercepts$median_PBI_Var) +
# geom_richtext(aes(x = 0.4 , y = 50),label = "Low Growth without Insertion")+
# geom_richtext(aes(x = 0.4 , y = 4800),label = "High Growth without Insertion")+
# geom_richtext(aes(x = 0.7 , y = 4800),label = "High Growth with Insertion")+
# geom_richtext(aes(x = 0.7 , y = 50),label = "Low Growth with Insertion")+
# labs(x = "Insertión in GVC", y = expression(paste("GDP Growth.", ~italic("1990 = 100%"))), color = " ") +
# scale_y_continuous(trans = "log10", limits = c(50, 5000), labels = function(x) paste0(x, "%")) +
# scale_x_continuous(labels = scales::percent) +
# scale_color_manual(values = c("GN" = "#616161", "GS" = "#BDBDBD")) +
# theme_minimal() +
# theme(
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
# legend.position = c(.10,.90))
#
# Figure_3.1
# rm(Figure_3.1)
Figure_3 <-PBI_CGV %>% arrange(Year) %>%
ggplot()+
geom_vline(xintercept = intercepts$median_Insertión)+
geom_hline(yintercept = intercepts$median_PBI_Var)+
geom_path(aes(x = Insertión, y = PBI_Var, group = iso3c),
arrow = arrow(length = unit(0.10, "inches")), lwd = 1.7,lineend = "round", linejoin = "round", color ="black")+
geom_path(aes(x = Insertión, y = PBI_Var, group = iso3c, color = NG_SG), arrow = arrow(length = unit(0.10, "inches")), lwd = 1.3, lineend = "round", linejoin = "round")+
geom_text_repel(data = PBI_CGV %>% filter(Year == 2018), aes(x = Insertión, y = PBI_Var, label = iso3c), size = 3,color = "black", fontface='bold')+
gghighlight() +
facet_wrap(~ cuadrant)+
labs(x = "Insertión in GVC", y = expression(paste("GDP Growth.", ~italic("1990 = 100%"))), color = " ") +
scale_y_continuous(trans = "log10", limits = c(50, 5000), labels = function(x) paste0(x, "%")) +
scale_x_continuous(labels = scales::percent) +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "bottom",
strip.text.x = element_text(size = 10, color = "black", face = "bold"))
Figure_3 
PBI_CGV_E <- PBI_CGV %>%
filter (cuadrant == "Low Growth without Insertion") %>%
group_by(Country) %>%
summarise(n())
PBI_CGV_E <- PBI_CGV %>%
filter (cuadrant == "Low Growth without Insertion") %>%
summarise(mean(Insertión),
mean(PBI_Var))PBI_CGV_E<-PBI_CGV %>%
select(iso3c,Year,Insertión,PBI_Var) %>%
unite(col = "country", c("iso3c","Year"), sep = "_") %>%
mutate(PBI_Var = PBI_Var,
PBI_Var2 = log(PBI_Var)) %>%
filter(!grepl("1990", country))
PBI_CGV_E<-PBI_CGV %>%
select(iso3c,Year,Insertión,PBI_Var) %>%
unite(col = "country", c("iso3c","Year"), sep = "_") %>%
mutate(PBI_Var = PBI_Var,
PBI_Var2 = log(PBI_Var)) %>%
filter(!grepl("1990", country))
WSS <- fviz_nbclust(scale(PBI_CGV_E[,-c(1,3)]), kmeans, method = "wss") + ggtitle("WSS") +
theme(plot.title = element_text(face = "bold"))
Silouttes <- fviz_nbclust(scale(PBI_CGV_E[,-c(1,3)]), kmeans, method = "silhouette") + ggtitle("Silhouette") +
theme(plot.title = element_text(face = "bold"))
Cetreoide <- grid.arrange(WSS, Silouttes, ncol = 2)
km <- kmeans(scale(PBI_CGV_E[,-c(1,3)]), centers = 3, nstart = 50)
PBI_CGV_E<-PBI_CGV_E %>%
cbind(km$cluster) %>%
rename("cluster"="km$cluster" ) %>%
mutate(cluster = case_when( cluster == 1 ~ "Cluster 1",
cluster == 2 ~ "Cluster 2",
cluster == 3 ~ "Cluster 3"))
Table_3 <- PBI_CGV_E %>%
mutate(cluster = case_when(
cluster == "Cluster 1" ~ 1 ,
cluster == "Cluster 2" ~ 2 ,
cluster == "Cluster 3" ~ 3
)) %>%
separate("country", into = c("iso3c", "year"), sep = "_") %>%
left_join(list) %>%
select(iso3c, year, cluster, NG_SG) %>%
pivot_wider(names_from = year, values_from = cluster) %>%
select("iso3c", "1995","2000","2008","2018", "NG_SG") %>%
unite(col = "Trayectory", c("1995","2000","2008","2018"), sep=",") %>%
group_by(Trayectory) %>%
summarise(
Cant. = n(),
Countries = paste(iso3c, collapse = ", "),
Count_NG = sum(NG_SG == "GN"),
Count_SG = sum(NG_SG == "GS")
) %>%
arrange(desc(Cant.)) %>%
mutate(
count_total_NG = sum(Count_NG),
count_total_SG = sum(Count_SG),
Proportion_SG = sprintf("%.2f%%", (Count_SG / count_total_SG) * 100),
Proportion_NG = sprintf("%.2f%%", (Count_NG / count_total_NG) * 100),
"N°" = row_number()
) %>%
select("N°", "Trayectory", "Cant.", "Countries", "Proportion_NG", "Proportion_SG")
Table_3## # A tibble: 12 × 6
## `N°` Trayectory Cant. Countries Proportion_NG Proportion_SG
## <int> <chr> <int> <chr> <chr> <chr>
## 1 1 3,3,2,2 13 IND, BRA, AUS, MEX, IDN, … 5.00% 42.86%
## 2 2 3,3,1,1 10 DEU, GBR, FRA, RUS, ESP, … 40.00% 7.14%
## 3 3 1,1,1,1 8 NLD, BEL, AUT, NOR, IRL, … 25.00% 10.71%
## 4 4 1,1,1,2 3 POL, MYS, PHL 0.00% 10.71%
## 5 5 3,3,1,2 3 KOR, THA, ROU 0.00% 10.71%
## 6 6 3,3,3,2 3 USA, ARG, BGD 5.00% 7.14%
## 7 7 3,1,1,1 2 SWE, DNK 10.00% 0.00%
## 8 8 3,3,3,3 2 JPN, CAN 10.00% 0.00%
## 9 9 2,2,2,2 1 VNM 0.00% 3.57%
## 10 10 3,2,2,2 1 CHN 0.00% 3.57%
## 11 11 3,3,1,3 1 ITA 5.00% 0.00%
## 12 12 3,3,2,3 1 IRN 0.00% 3.57%Table_2 <- aggregate(PBI_CGV_E[-4], by=list(cluster=km$cluster), mean)Figure_8 <- PBI_CGV_E %>%
ggplot(aes(x = Insertión, y = PBI_Var, colour = factor(cluster))) +
geom_point(size = 3, color = "black") +
geom_point(size = 1.5) +
stat_ellipse(type = "norm", linetype = 2) +
stat_ellipse(type = "t", geom = "polygon",
aes(fill = factor(cluster)), alpha = 0.25) +
geom_text_repel(data = PBI_CGV_E %>% filter(!grepl("1990", country)), aes(label = country), color = "black", size = 2.5)+
gghighlight() +
facet_wrap(vars(cluster)) + # Se corrige aquÃ
labs(x = "Insertión in GVC", y = expression(paste("GDP Growth.", ~italic("1990 = 100%")))) +
scale_y_continuous(trans = "log10", labels = function(x) paste0(x, "%")) +
scale_x_continuous(labels = scales::percent) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "none",
strip.text.x = element_text(size = 10, color = "black", face = "bold"))
Figure_8
table_3_png <- tableGrob(Table_3)
table_2_png <- tableGrob(Table_2)
ggsave(filename = "Figure_1.png", plot = Figure_1, bg = "white", width = 12, height = 6)
ggsave(filename = "Figure_2.png", plot = Figure_2, bg = "white", width = 12, height = 6)
ggsave(filename = "Figure_3.png", plot = Figure_3, bg = "white", width = 12, height = 12)
ggsave(filename = "Figure_5.png", plot = Figure_8, bg = "white", width = 12, height = 8)
ggsave(filename = "Figure_4.png", plot = Cetreoide, bg = "white", width = 10, height = 4)
ggsave("table_3.png", table_3_png , width = 14, height = 4, dpi = 300)
ggsave("table_2.png", table_2_png , width = 10, height = 4, dpi = 300)