Individual Assignment
Ansh Jain
2024-12-07
1. Problem Definition
Title: Total Factor Productivity and Economic Growth: Insights from Penn World Table
Objective:
This assignment examines the role of Total Factor Productivity (TFP) in driving GDP growth across countries. The analysis explores whether countries with higher TFP experience faster economic growth and whether disparities in productivity contribute to differences in living standards.
2. Data Wrangling
#Data Source: Penn World Table (PWT 10.0)
Key Variables:
rgdpna: Real GDP at constant prices. pop: Population. ctfp: Total Factor Productivity. hc: Human Capital Index (an additional explanatory variable).
#Tools and Commands: Filter for a specific year to analyze cross-sectional data (e.g., 2019). Create new variables like GDP per capita and TFP-adjusted GDP.
# Loading 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# Import Dataset
library(readxl)
pwt <- read_excel("C:/Users/VANSH JAIN/Downloads/pwt1001.xlsx",
sheet = "Data")
View(pwt)# Filter data for the year 2019 and select relevant columns
filtered_data <- pwt %>%
filter(year == 2019) %>%
select(country, rgdpna, pop, ctfp, hc)
filtered_data## # A tibble: 183 × 5
## country rgdpna pop ctfp hc
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Aruba 3069. 0.106 NA NA
## 2 Angola 222151. 31.8 0.388 1.48
## 3 Anguilla 223. 0.0149 NA NA
## 4 Albania 37205. 2.88 NA 2.96
## 5 United Arab Emirates 647986. 9.77 NA 2.75
## 6 Argentina 975569 44.8 0.829 3.10
## 7 Armenia 43260. 2.96 0.838 3.14
## 8 Antigua and Barbuda 1512. 0.0971 NA NA
## 9 Australia 1315734. 25.2 0.838 3.55
## 10 Austria 476723. 8.96 0.829 3.38
## # ℹ 173 more rows# Create GDP per capita and TFP-adjusted GDP
processed_data <- filtered_data %>%
mutate(
gdp_per_capita = rgdpna / pop,
tfp_adjusted_gdp = ctfp * rgdpna)
processed_data## # A tibble: 183 × 7
## country rgdpna pop ctfp hc gdp_per_capita tfp_adjusted_gdp
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Aruba 3.07e3 0.106 NA NA 28865. NA
## 2 Angola 2.22e5 31.8 0.388 1.48 6980. 86194.
## 3 Anguilla 2.23e2 0.0149 NA NA 15028. NA
## 4 Albania 3.72e4 2.88 NA 2.96 12914. NA
## 5 United Arab Emir… 6.48e5 9.77 NA 2.75 66320. NA
## 6 Argentina 9.76e5 44.8 0.829 3.10 21785. 808317.
## 7 Armenia 4.33e4 2.96 0.838 3.14 14626. 36265.
## 8 Antigua and Barb… 1.51e3 0.0971 NA NA 15564. NA
## 9 Australia 1.32e6 25.2 0.838 3.55 52205. 1102123.
## 10 Austria 4.77e5 8.96 0.829 3.38 53235. 395302.
## # ℹ 173 more rows3. Table Output
Objective:
Highlight the contribution of TFP to GDP across countries, compare averages by region, and rank countries by their TFP levels.
# Summarize TFP and GDP per capita by country
summary_table <- processed_data %>%
arrange(desc(ctfp)) %>%
select(country, ctfp, gdp_per_capita, tfp_adjusted_gdp)
summary_table## # A tibble: 183 × 4
## country ctfp gdp_per_capita tfp_adjusted_gdp
## <chr> <dbl> <dbl> <dbl>
## 1 Mauritius 1.28 23827. 38630.
## 2 China, Macao SAR 1.25 91871. 73502.
## 3 Egypt 1.23 12826. 1579588.
## 4 Trinidad and Tobago 1.11 25589. 39524.
## 5 Norway 1.05 70222. 395835.
## 6 Luxembourg 1.04 91865. 58800.
## 7 United States 1 62491. 20563592
## 8 Switzerland 0.967 75455. 626798.
## 9 Iceland 0.967 52017. 17046.
## 10 Netherlands 0.937 56195. 900658.
## # ℹ 173 more rows# Display the top rows
head(summary_table)## # A tibble: 6 × 4
## country ctfp gdp_per_capita tfp_adjusted_gdp
## <chr> <dbl> <dbl> <dbl>
## 1 Mauritius 1.28 23827. 38630.
## 2 China, Macao SAR 1.25 91871. 73502.
## 3 Egypt 1.23 12826. 1579588.
## 4 Trinidad and Tobago 1.11 25589. 39524.
## 5 Norway 1.05 70222. 395835.
## 6 Luxembourg 1.04 91865. 58800.4. Data Visualization
Objective:
Visualize the relationship between TFP and GDP per capita, and identify high-performing countries.
Visualization Ideas:
Scatter Plot: TFP vs. GDP per capita, with countries color-coded by region. Bar Chart: Top 10 countries by TFP. Map: Global distribution of TFP levels (if geographic coordinates are available).
# Installing library
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.4.2# Scatter plot: TFP vs GDP per Capita
ggplot(processed_data, aes(x = ctfp, y = gdp_per_capita, label = country)) +
geom_point(aes(color = country), size = 3) +
geom_text(size = 3, hjust = 0.5, vjust = -1) +
labs(
title = "TFP vs GDP per Capita (2019)",
x = "Total Factor Productivity",
y = "GDP per Capita"
) +
theme_minimal()## Warning: Removed 65 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 65 rows containing missing values or values outside the scale range
## (`geom_text()`).
5. Summary and Interpretation
Findings:
Strong Correlation: Higher TFP correlates positively with GDP per capita, underscoring the importance of productivity in economic performance. Top Performers: Countries with high TFP consistently outperform others in GDP per capita. Policy Implications: Nations with lower TFP should prioritize investments in innovation, education, and infrastructure to boost productivity.
Implications:
This analysis highlights the critical role of TFP in economic development. Businesses and policymakers can leverage these insights to identify high-productivity regions for investment and collaboration.