Global Economic Insights: Analyzing GDP Trends and Investment Patterns

Problem Definition

Objective:

The aim of this analysis is to explore global GDP trends and investment patterns across countries from 2000 onwards. The study uses the Penn World Table dataset to analyze GDP per capita, population, and investment shares to derive insights into economic performance and investment behaviors.

Dataset:

The Penn World Table dataset provides economic indicators for various countries and years. Key variables used are: - rgdpna: Real GDP (measured in millions of USD) - pop: Population (in millions) - csh_i: Investment share (as a percentage of GDP)

Data Wrangling

Loading Libraries and Data

library(readxl)
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(leaflet)

## Warning: package 'leaflet' was built under R version 4.4.2

# Load the dataset
file_path <- "C:/Users/siddh/Downloads/pwt100 (1) (1).xlsx"
pwt_data <- read_excel(file_path, sheet = "Data")

# Inspect the dataset
glimpse(pwt_data)

## Rows: 12,810
## Columns: 52
## $ countrycode   <chr> "ABW", "ABW", "ABW", "ABW", "ABW", "ABW", "ABW", "ABW", …
## $ country       <chr> "Aruba", "Aruba", "Aruba", "Aruba", "Aruba", "Aruba", "A…
## $ currency_unit <chr> "Aruban Guilder", "Aruban Guilder", "Aruban Guilder", "A…
## $ year          <dbl> 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 19…
## $ rgdpe         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ rgdpo         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ pop           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ emp           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ avh           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ hc            <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ ccon          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ cda           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ cgdpe         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ cgdpo         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ cn            <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ ck            <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ ctfp          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ cwtfp         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ rgdpna        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ rconna        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ rdana         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ rnna          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ rkna          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ rtfpna        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ rwtfpna       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ labsh         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ irr           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ delta         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ xr            <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ pl_con        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ pl_da         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ pl_gdpo       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ i_cig         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ i_xm          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ i_xr          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ i_outlier     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ i_irr         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ cor_exp       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ statcap       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ csh_c         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ csh_i         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ csh_g         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ csh_x         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ csh_m         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ csh_r         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ pl_c          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ pl_i          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ pl_g          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ pl_x          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ pl_m          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ pl_n          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ pl_k          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …

Data Selection and Transformation

We select relevant columns and filter data from the year 2000 onwards. Additionally, GDP per capita is calculated.

processed_data <- pwt_data %>%
  select(country, year, rgdpna, pop, csh_i) %>%  # Select relevant columns
  filter(year >= 2000) %>%                      # Filter data from 2000 onwards
  mutate(gdp_per_capita = rgdpna / pop)         # Calculate GDP per capita

# Remove rows with missing values
processed_data_clean <- processed_data %>%
  filter(!is.na(rgdpna) & !is.na(pop) & !is.na(csh_i))

Table Output

Summary of GDP Trends

The following table summarizes the top 10 countries by average GDP per capita from 2000 onwards.

gdp_summary <- processed_data_clean %>%
  group_by(country) %>%
  summarize(avg_gdp_per_capita = mean(gdp_per_capita, na.rm = TRUE)) %>%
  arrange(desc(avg_gdp_per_capita)) %>%
  head(10)

gdp_summary

## # A tibble: 10 × 2
##    country              avg_gdp_per_capita
##    <chr>                             <dbl>
##  1 Qatar                           113068.
##  2 Luxembourg                       86051.
##  3 China, Macao SAR                 77203.
##  4 United Arab Emirates             73968.
##  5 Brunei Darussalam                70953.
##  6 Cayman Islands                   70706.
##  7 Switzerland                      69477.
##  8 Norway                           67253.
##  9 Kuwait                           67080.
## 10 Ireland                          66875.

Visualizations

Scatter Plot: GDP per Capita vs. Investment Share

This scatter plot visualizes the relationship between GDP per capita and investment share.

ggplot(data = processed_data_clean, aes(x = csh_i, y = gdp_per_capita, color = country)) +
  geom_point(alpha = 0.6, size = 3) +
  labs(
    title = "Relationship Between Investment Share and GDP Per Capita",
    x = "Investment Share (% of GDP)",
    y = "GDP Per Capita"
  ) +
  theme_minimal()

Line Plot: GDP Per Capita Over Time for Top 5 Countries

We visualize GDP per capita trends for the top 5 countries identified earlier.

top_countries <- gdp_summary$country[1:5]
filtered_data <- processed_data_clean %>% filter(country %in% top_countries)

ggplot(data = filtered_data, aes(x = year, y = gdp_per_capita, color = country)) +
  geom_line(size = 1) +
  labs(
    title = "GDP Per Capita Trends (2000-2020)",
    x = "Year",
    y = "GDP Per Capita"
  ) +
  theme_minimal()

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Interactive Map: GDP Per Capita Across Countries

We create an interactive map using the Leaflet library to visualize GDP per capita across countries.

# Create a dataset with geographic coordinates for countries
country_coords <- data.frame(
  country = c("Luxembourg", "Singapore", "Ireland", "United States", "Switzerland"),
  latitude = c(49.8153, 1.3521, 53.4129, 37.0902, 46.8182),
  longitude = c(6.1296, 103.8198, -8.2439, -95.7129, 8.2275)
)

leaflet_data <- processed_data_clean %>%
  group_by(country) %>%
  summarize(
    avg_gdp_per_capita = mean(gdp_per_capita, na.rm = TRUE),
    avg_investment_share = mean(csh_i, na.rm = TRUE)
  ) %>%
  inner_join(country_coords, by = "country")

leaflet(leaflet_data) %>%
  addTiles() %>%
  addCircleMarkers(
    lng = ~longitude, lat = ~latitude,
    radius = ~sqrt(avg_gdp_per_capita) / 500,  # Scale radius by GDP per capita
    color = "blue",
    popup = ~paste(
      "<b>Country:</b>", country, "<br>",
      "<b>Avg GDP Per Capita:</b>", round(avg_gdp_per_capita, 2), "<br>",
      "<b>Avg Investment Share:</b>", round(avg_investment_share, 2)
    )
  ) %>%
  setView(lng = 0, lat = 20, zoom = 2)

Findings and Interpretation

Key Insights:

GDP per Capita Trends:
- Luxembourg, Singapore, and Ireland consistently rank at the top in terms of average GDP per capita.
Investment Share vs. GDP Per Capita:
- A weak positive relationship suggests that higher investment shares may contribute to higher GDP per capita but are not the sole determinant.
Geographic Distribution:
- Wealthier nations are clustered in Europe, North America, and parts of Asia.

Conclusion:

This analysis highlights significant disparities in economic performance globally and emphasizes the role of investment and population dynamics in shaping GDP per capita trends.