Economic Insights from the Penn World Table: Trends and Comparisons

Rahul Kumar

2024-12-07

Economic Insights from the Penn World Table: Trends and Comparisons

Author: Rahul Kumar
Enrolment: M2024ANLT021 Date: 2024-12-07
Output: HTML Document

Problem Definition

In this assignment, we will use the Penn World Table (PWT) dataset to analyze key economic trends and comparisons among countries. The main focus will be on:

  1. The relationship between GDP and capital investment.
  2. The evolution of employment rates by region.
  3. Comparing growth trends in GDP per capita for various economic blocs.
  4. Analyzing the relationship between labor productivity and consumption expenditure.

Loading 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(tidyr)
## Warning: package 'tidyr' was built under R version 4.4.2
library(readxl)
## Warning: package 'readxl' was built under R version 4.4.2
library(rnaturalearth)
## Warning: package 'rnaturalearth' was built under R version 4.4.2
library(rnaturalearthdata)
## Warning: package 'rnaturalearthdata' was built under R version 4.4.2
## 
## Attaching package: 'rnaturalearthdata'
## The following object is masked from 'package:rnaturalearth':
## 
##     countries110
library(sf)    # For spatial data manipulation
## Warning: package 'sf' was built under R version 4.4.2
## Linking to GEOS 3.12.2, GDAL 3.9.3, PROJ 9.4.1; sf_use_s2() is TRUE
library(scales) # For formatting scales
## Warning: package 'scales' was built under R version 4.4.2

Loading the Penn World Table Dataset

# Load the Penn World Table data
pwt <- read_excel("C:/Users/sharm/Downloads/pwt100 (1).xlsx", sheet = "Data")

# Check the structure of the dataset
str(pwt)
## tibble [12,810 × 52] (S3: tbl_df/tbl/data.frame)
##  $ countrycode  : chr [1:12810] "ABW" "ABW" "ABW" "ABW" ...
##  $ country      : chr [1:12810] "Aruba" "Aruba" "Aruba" "Aruba" ...
##  $ currency_unit: chr [1:12810] "Aruban Guilder" "Aruban Guilder" "Aruban Guilder" "Aruban Guilder" ...
##  $ year         : num [1:12810] 1950 1951 1952 1953 1954 ...
##  $ rgdpe        : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ rgdpo        : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ pop          : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ emp          : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ avh          : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ hc           : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ ccon         : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ cda          : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ cgdpe        : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ cgdpo        : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ cn           : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ ck           : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ ctfp         : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ cwtfp        : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ rgdpna       : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ rconna       : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ rdana        : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ rnna         : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ rkna         : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ rtfpna       : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ rwtfpna      : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ labsh        : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ irr          : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ delta        : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ xr           : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ pl_con       : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ pl_da        : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ pl_gdpo      : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ i_cig        : chr [1:12810] NA NA NA NA ...
##  $ i_xm         : chr [1:12810] NA NA NA NA ...
##  $ i_xr         : chr [1:12810] NA NA NA NA ...
##  $ i_outlier    : chr [1:12810] NA NA NA NA ...
##  $ i_irr        : chr [1:12810] NA NA NA NA ...
##  $ cor_exp      : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ statcap      : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ csh_c        : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ csh_i        : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ csh_g        : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ csh_x        : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ csh_m        : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ csh_r        : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ pl_c         : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ pl_i         : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ pl_g         : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ pl_x         : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ pl_m         : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ pl_n         : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...
##  $ pl_k         : num [1:12810] NA NA NA NA NA NA NA NA NA NA ...

Data Wrangling

We’ll select relevant columns and create new variables for analysis.

# Select relevant columns and create GDP per capita and labor productivity
pwt_clean <- pwt %>%
  select(country, year, rgdpna, pop, emp, ccon, ck) %>%
  filter(!is.na(rgdpna)) %>%
  mutate(
    gdp_per_capita = rgdpna / pop,
    labor_productivity = rgdpna / emp,
    employment_rate = emp / pop
  )

New Visualizations

1. GDP vs. Capital Investment in 2017

This scatter plot shows the relationship between GDP and capital investment across countries in 2017.

2. Employment Rates by Region in 2017

A choropleth map showing employment rates across the world in 2017.

3. GDP Per Capita Trends by Economic Bloc

A line chart comparing GDP per capita trends for three economic blocs: the European Union, BRICS, and ASEAN.

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

4. Labor Productivity vs. Consumption Expenditure in 2017

A scatter plot comparing labor productivity and consumption expenditure for different countries in 2017.

Summary of Findings

  1. GDP vs. Capital Investment in 2017:
    The scatter plot shows a positive correlation between GDP and capital investment. Wealthier nations with higher GDP per capita tend to have greater capital stock, highlighting the role of investment in economic growth.

  2. Employment Rates by Region in 2017:
    The global map illustrates clear regional disparities in employment rates. Developed regions like Europe and North America have higher employment rates compared to parts of Africa and Asia, indicating differences in labor market conditions.

  3. GDP Per Capita Trends by Economic Bloc:
    The line chart reveals that the European Union maintains consistently high GDP per capita. BRICS countries show rapid growth, especially China and India, while ASEAN countries display moderate but steady growth trends.

  4. Labor Productivity vs. Consumption Expenditure in 2017:
    The scatter plot suggests that countries with higher labor productivity also tend to have higher consumption expenditure. This highlights the relationship between economic efficiency and household spending power.

This analysis underscores key insights into economic growth, employment, and productivity using the Penn World Table dataset.