“Significant economic disparities exist between developed and developing countries hindering equitable global economic growth.”

The Penn World Table (PWT) is a comprehensive database providing national accounts data for macroeconomic analysis. It offers comparable measures of economic activity, such as GDP, productivity, and real income levels, across countries and over time. Researchers use it to study global economic growth, income inequality, and international comparisons. Here, we are analysing GDP, GDP components, Labour productivity, capital intensity, capital stock and average labour force participation in measure economies of the world like USA,China,India,UK,France, Germany and Japan over a period from 1990 to 2017. The time period is chosen which is after globalisation in India. This analysis will help us track development and trends among these major economies over a period of time.

Importing dataset and required Libraries

library(readxl)
library(dplyr)
library(ggplot2)
library(scales)
library(tidyr)
library(DT)
pwt <- read_excel("C:/Users/ranja/Downloads/pwt.xlsx", sheet = "Data")
head(pwt)
## # A tibble: 6 × 52
##   countrycode country currency_unit   year rgdpe rgdpo   pop   emp   avh    hc
##   <chr>       <chr>   <chr>          <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ABW         Aruba   Aruban Guilder  1950    NA    NA    NA    NA    NA    NA
## 2 ABW         Aruba   Aruban Guilder  1951    NA    NA    NA    NA    NA    NA
## 3 ABW         Aruba   Aruban Guilder  1952    NA    NA    NA    NA    NA    NA
## 4 ABW         Aruba   Aruban Guilder  1953    NA    NA    NA    NA    NA    NA
## 5 ABW         Aruba   Aruban Guilder  1954    NA    NA    NA    NA    NA    NA
## 6 ABW         Aruba   Aruban Guilder  1955    NA    NA    NA    NA    NA    NA
## # ℹ 42 more variables: ccon <dbl>, cda <dbl>, cgdpe <dbl>, cgdpo <dbl>,
## #   cn <dbl>, ck <dbl>, ctfp <dbl>, cwtfp <dbl>, rgdpna <dbl>, rconna <dbl>,
## #   rdana <dbl>, rnna <dbl>, rkna <dbl>, rtfpna <dbl>, rwtfpna <dbl>,
## #   labsh <dbl>, irr <dbl>, delta <dbl>, xr <dbl>, pl_con <dbl>, pl_da <dbl>,
## #   pl_gdpo <dbl>, i_cig <chr>, i_xm <chr>, i_xr <chr>, i_outlier <chr>,
## #   i_irr <chr>, cor_exp <dbl>, statcap <dbl>, csh_c <dbl>, csh_i <dbl>,
## #   csh_g <dbl>, csh_x <dbl>, csh_m <dbl>, csh_r <dbl>, pl_c <dbl>, …

LABOUR PRODUCTIVITY AND GDP PER CAPITA

pwt <- pwt %>%
  mutate(labor_productivity = rgdpo / emp,
         gdp_per_capita = rgdpe / pop)

# Filter for selected countries and year
selected_countries <- c("United States", "China", "India", "United Kingdom", "France", "Germany","Japan")
selected_data <- pwt %>%
  filter(year >= 1990 & year <= 2017) %>%
  filter(country %in% selected_countries)

# Line plot for labor productivity and GDP_per_capita over time
ggplot(selected_data, aes(x = year, group = country)) +
  geom_line(aes(y = labor_productivity, color = "Labor Productivity"), size = 1.0) +
  geom_line(aes(y = gdp_per_capita, color = "GDP per Capita"), size = 1.0) +
  facet_wrap(~country, scales = "free_y") +
  labs(title = "Labor Productivity and GDP Per Capita Over Time (1990-2017)",
       x = "Year",
       y = "Value",
       color = "Metric") +
  scale_color_manual(values = c("Labor Productivity" = "gold", "GDP per Capita" = "red")) +
  theme_minimal() +
  theme(legend.position = "top", strip.text = element_text(size = 10))

Analysis

Labor productivity (yellow) shows a steady increase across all countries, with the United States and Germany demonstrating consistently high levels, indicative of advanced technological and organizational efficiency. China and India show sharp growth in labor productivity, reflecting rapid industrialization and economic reforms during this period. GDP per capita (red) also increases across all nations, with the United States having the highest values, followed by Germany, Japan, and the United Kingdom, representing their developed economies. China’s GDP per capita shows remarkable growth, although it remains lower than developed nations, while India has the lowest levels of GDP per capita and labor productivity, highlighting the ongoing challenges of economic development and productivity enhancement. This data emphasizes the correlation between labor productivity and GDP per capita growth, with variances influenced by levels of industrialization and economic maturity.

GDP COMPONENTS ANALYSIS

selected_data <- pwt %>%
  filter(year >= 1990 & year <= 2017) %>%
  filter(country %in% selected_countries) %>%
  select(country, year, ccon, cda, ctfp)

# Convert to long format for stacking
gdp_data <- selected_data %>%
  gather(key = "component", value = "value", ccon, cda)

# Stacked area chart for GDP components
ggplot(gdp_data, aes(x = year, y = value, fill = component)) +
  geom_area(alpha = 0.8) +
  facet_wrap(~ country, scales = "free_y") +
  labs(title = "Breakdown of GDP Components (1990-2017)",
       x = "Year",
       y = "GDP Components (in million 2017 US$)",
       fill = "Component") +
  scale_fill_manual(values = c("ccon" = "blue", "cda" = "orange"), labels = c("Consumption", "Investment")) +
  scale_y_continuous(labels = label_comma()) +
  theme_minimal() +
  theme(legend.position = "bottom")

Analysis

Across all countries, consumption (blue) consistently constitutes the largest share of GDP, while investment (orange) forms a smaller, yet significant portion. China’s GDP demonstrates a steep increase in both consumption and investment, with investment showing notable growth, reflecting its rapid economic expansion during the period. India also exhibits significant growth in GDP components, though at a lower scale than China. Developed nations like the United States, Germany, Japan, and the United Kingdom show steady but less dramatic increases in GDP components, indicative of mature and stable economies. France follows a similar trend. These patterns highlight the contrasting dynamics between rapidly growing economies (China and India) and developed ones, where consumption drives growth while investment plays a more supportive role.

CAPITAL INTENSITY OVER TIME

# Calculate capital intensity
selected_data <- pwt %>%
  filter(year >= 1990 & year <= 2017) %>%
  filter(country %in% selected_countries) %>%
  mutate(capital_intensity = ck / emp)

# Line plot for capital intensity over time
ggplot(selected_data, aes(x = year, y = capital_intensity, color = country)) +
  geom_line(size = 1) +
  labs(title = "Capital Intensity Over Time (1990-2017)",
       x = "Year",
       y = "Capital Intensity (Capital Stock per Worker)",
       color = "Country") +
  theme_minimal() +
  theme(legend.position = "bottom")

Analysis

Developed nations such as the United States and Japan consistently maintain higher levels of capital intensity, reflecting mature economies with stable capital investment practices. In contrast, developing countries like India and China show significant upward trends, particularly after 2000, indicating rapid economic growth and increasing capital investments relative to their labor forces. European nations, including Germany, France, and the United Kingdom, display slight declines or stability, possibly due to economic restructuring or saturation in capital investments. Overall, the visualization highlights the economic disparities between developing and developed nations, with India and China gradually narrowing the gap through increased investments. These trends emphasize the critical role of capital intensity in shaping economic growth and productivity globally.

AVERAGE CAPITAL STOCK BY COUNTRY

# Calculate average capital stock
average_capital_data <- selected_data %>%
  group_by(country) %>%
  summarise(avg_capital_stock = mean(cn, na.rm = TRUE))

# Bar chart for average capital stock by country
ggplot(average_capital_data, aes(x = reorder(country, avg_capital_stock), y = avg_capital_stock, fill = country)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(title = "Average Capital Stock by Country (1990-2017)",
       x = "Country",
       y = "Average Capital Stock (in million 2017 US$)") +
  scale_fill_viridis_d() +
  scale_y_continuous(labels = label_comma())+
  theme_minimal() +
  theme(legend.position = "none")

Analysis

The United States leads by a significant margin, reflecting its advanced economic infrastructure and substantial investment in capital stock. China ranks second, showcasing its rapid industrialization and economic growth during this period. Japan and Germany follow, representing high levels of capital stock typical of developed economies. Among developing nations, India has a lower average capital stock, reflecting the gap in investment levels compared to developed countries. Despite being developed, France and the United Kingdom rank lower than Germany, suggesting differences in investment priorities or structural economic factors.The findings highlight the stark differences in average capital stock between developed and developing economies, with the United States and China emerging as leaders. The visualization underscores the importance of capital investment in driving economic growth and highlights regional variations based on industrial development and economic policies.

AVERAGE LABOUR FORCE PARTICIPATION

 # Calculating labor force participation rate (%)
selected_data <- pwt %>%
  filter(year >= 1990 & year <= 2017) %>% 
  filter(country %in% selected_countries) %>% 
  select(country, year, pop, emp, avh, rgdpo) %>%  # Selecting relevant variables
  mutate(labor_force_participation = (emp / pop) * 100,
         labor_productivity = rgdpo / emp) 


average_participation <- selected_data %>%
  group_by(country) %>%
  summarise(avg_lfp = mean(labor_force_participation, na.rm = TRUE))

ggplot(average_participation, aes(x = reorder(country, -avg_lfp), y = avg_lfp, fill = country)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(title = "Average Labor Force Participation Rate (1990-2017)",
       x = "Country",
       y = "Labor Force Participation Rate (%)") +
  scale_fill_viridis_d() +
  theme_minimal() +
  theme(legend.position = "none")

Analysis

Among these countries, China has the highest average labor force participation rate, significantly exceeding others. In contrast, India has the lowest participation rate. Developed nations such as the United States, Germany, Japan, and the United Kingdom exhibit moderately high rates but fall below China’s figures. France’s participation rate is relatively lower among the developed countries. This distribution suggests significant variations in labor force engagement, likely influenced by economic structures, demographic factors, and labor market policies across these nations.

DATA TABLE

selected_data <- pwt %>%
  filter(year >= 1990 & year <= 2017) %>%  # Filter for years of interest
  filter(country %in% selected_countries) %>%  # Filter for selected countries
  select(country, year, pop, emp, avh, hc,labor_productivity,gdp_per_capita)%>%
  group_by(country)

# Create an interactive table
datatable(selected_data, options = list(pageLength = 15, autoWidth = TRUE))

Conclusion

The analysis of capital intensity, average capital stock, labor force participation, GDP components, labor productivity, and GDP per capita reveals significant economic disparities between developed and developing nations. The United States and Japan maintain high capital intensity and average capital stock, reflecting their advanced economic structures. In contrast, China and India show impressive growth in capital intensity, GDP components, and labor productivity, with China particularly outpacing others in these areas due to rapid industrialization. Labor force participation rates vary widely, with China having the highest and India the lowest. Developed nations show steady, stable growth in GDP, driven primarily by consumption, while investment plays a more significant role in rapidly growing economies like China. GDP per capita and labor productivity are highest in developed nations, with China making remarkable strides, although it still lags behind. These findings highlight the ongoing gap between developed and developing economies, emphasizing the critical role of capital investment, labor productivity, and industrialization in shaping economic growth and development.