Text for the Assignment

Problem Definition

Title: “Economic Performance Analysis of G20 Countries Post Formation Using the Penn World Table”

Objective:
The aim of this analysis is to evaluate the economic performance of G20 countries since the group’s formation in 1999. Key indicators such as GDP growth, GDP per capita, and human capital index are analyzed using the Penn World Table to identify trends and insights into economic developments in member nations.

Data Wrangling

  1. Dataset Used:
    The Penn World Table (version 10.01) provides comprehensive economic data for countries globally, making it suitable for examining G20 nations’ performance.

  2. Steps Taken:

    • Filtering: Data was filtered to include only G20 countries and years post-1999.
    • Selection: Relevant variables such as GDP (output and expenditure), population, and human capital were selected.
    • Mutations:
      • Calculated year-over-year GDP growth rates.
      • Derived GDP per capita by dividing GDP (output) by population.

  3. Tools Used:

    • Data wrangling was performed using the dplyr library for operations like filter, select, and mutate.

Table Output

A summary table was generated to showcase the following: - Average GDP Growth: Average annual GDP growth rates for each G20 country. - GDP Per Capita: Average GDP per capita to reflect living standards. - Human Capital Index: Average human capital levels as a measure of skill and education.

The data was arranged in descending order of average GDP growth to highlight top-performing countries.

Visualization

  1. GDP Growth Trends:
    A line chart was plotted to visualize GDP growth trends for G20 countries over the years.

  2. Country Comparisons:
    A bar chart compared the average GDP growth of G20 countries, providing a clear picture of relative performance.

  3. GDP Per Capita vs. Human Capital:
    A scatter plot illustrated the relationship between GDP per capita and human capital index, offering insights into how skill levels influence economic output.

Findings and Interpretation

  1. Summary:
    • The analysis reveals variations in GDP growth rates among G20 countries, with some nations demonstrating consistent growth trends.
    • Higher GDP per capita was often correlated with higher human capital, underscoring the importance of education and skills in economic performance.
  2. Insights:
    • Emerging economies like India and China showed robust growth, reflecting their development trajectories.
    • Developed nations maintained stability but exhibited slower growth due to saturated markets.
    • Investments in human capital were found to play a crucial role in sustaining economic output.

Conclusion

This analysis provided valuable insights into the economic performance of G20 nations over the past two decades. The findings emphasize the importance of GDP growth, human capital, and living standards in shaping a country’s economic trajectory. Further studies could focus on external factors like trade policies and global economic events to enrich the understanding of performance trends.

Now code Part

# Load necessary libraries
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.4.2
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.4.2
library(readxl)
## Warning: package 'readxl' was built under R version 4.4.2
# Load the Penn World Table Excel file

data <- read_excel("C:\\Users\\VANSH JAIN\\Downloads\\pwt1001.xlsx",sheet="Data")

# Define G20 countries
g20_countries <- c("Argentina", "Australia", "Brazil", "Canada", "China", "France", "Germany", 
                   "India", "Indonesia", "Italy", "Japan", "Mexico", "Russia", "Saudi Arabia", 
                   "South Africa", "South Korea", "Turkey", "United Kingdom", "United States")

# Filter for G20 countries and years after 1999
g20_data <- data %>%
  filter(country %in% g20_countries & year > 1999) %>%
  select(country, year, rgdpe, rgdpo, pop, hc)  # Select relevant columns

# Add new variables: GDP growth rate and GDP per capita
g20_data <- g20_data %>%
  group_by(country) %>%
  mutate(
    GDP_growth = (rgdpo - lag(rgdpo)) / lag(rgdpo) * 100,
    GDP_per_capita = rgdpo / pop
  ) %>%
  ungroup()

# Summarize data: Average GDP growth and GDP per capita by country
summary_table <- g20_data %>%
  group_by(country) %>%
  summarize(
    Avg_GDP_growth = mean(GDP_growth, na.rm = TRUE),
    Avg_GDP_per_capita = mean(GDP_per_capita, na.rm = TRUE),
    Avg_Human_Capital = mean(hc, na.rm = TRUE)
  ) %>%
  arrange(desc(Avg_GDP_growth))

# Display the summary table
print(summary_table)
## # A tibble: 17 × 4
##    country        Avg_GDP_growth Avg_GDP_per_capita Avg_Human_Capital
##    <chr>                   <dbl>              <dbl>             <dbl>
##  1 Saudi Arabia            7.92              43671.              2.48
##  2 India                   7.71               4286.              1.96
##  3 Indonesia               7.69               7529.              2.32
##  4 China                   7.24               9415.              2.48
##  5 Turkey                  5.54              19545.              2.23
##  6 Argentina               3.58              17978.              2.86
##  7 Brazil                  3.49              12870.              2.51
##  8 Australia               3.27              47794.              3.49
##  9 South Africa            2.74              12192.              2.49
## 10 Mexico                  2.65              16926.              2.60
## 11 Canada                  2.07              46307.              3.63
## 12 United States           2.03              55254.              3.68
## 13 United Kingdom          1.98              39747.              3.67
## 14 Germany                 1.86              44856.              3.63
## 15 France                  1.76              39243.              3.05
## 16 Italy                   1.02              37881.              2.97
## 17 Japan                   0.240             39225.              3.48
# Save the summary table as a CSV file
write.csv(summary_table, "G20_Summary_Table.csv", row.names = FALSE)

# Visualization 1: GDP growth trends over time for G20 countries
ggplot(g20_data, aes(x = year, y = GDP_growth, color = country)) +
  geom_line() +
  labs(title = "GDP Growth Trends for G20 Countries (Post-1999)",
       x = "Year", y = "GDP Growth (%)") +
  theme_minimal()
## Warning: Removed 17 rows containing missing values or values outside the scale range
## (`geom_line()`).

# Visualization 2: Bar chart of average GDP growth by country
ggplot(summary_table, aes(x = reorder(country, -Avg_GDP_growth), y = Avg_GDP_growth)) +
  geom_bar(stat = "identity", fill = "blue") +
  coord_flip() +
  labs(title = "Average GDP Growth of G20 Countries (Post-1999)",
       x = "Country", y = "Average GDP Growth (%)") +
  theme_minimal()

# Visualization 3: Scatter plot of GDP per capita vs. human capital
ggplot(g20_data, aes(x = GDP_per_capita, y = hc, color = country)) +
  geom_point() +
  labs(title = "GDP Per Capita vs. Human Capital Index (G20 Countries)",
       x = "GDP Per Capita", y = "Human Capital Index") +
  theme_minimal()