Human Capital and Economic Growth Analysis
RISHITA CHINTALA
2024-12-13
The analysis of various factors influencing economic growth and development. Specifically, we focus on understanding the relationship between GDP growth and the Human Capital Index (HCI) over a span of years (1990-2020) for different countries. By selecting and analyzing relevant data, we aim to provide insights into how these two factors—economic growth and human capital—interact at a global level and how they vary across countries.
##Objective he main objectives of this analysis are to analyze the average GDP growth over the years for different countries, focusing on the factors influencing economic growth across regions and how these factors have evolved. And investigate the Human Capital Index (HCI), which measures a country’s investment in education, skills, and health, and determine how these investments impact long-term growth and development.
##Data Selection For this analysis, the Penn World Table (PWT) version 10.0 dataset is used, which provides extensive global data on economic and human development indicators.
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, unionlibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 4.4.2library(leaflet)## Warning: package 'leaflet' was built under R version 4.4.2library(knitr)## Warning: package 'knitr' was built under R version 4.4.2knitr::opts_chunk$set(echo = TRUE)library(readxl)## Warning: package 'readxl' was built under R version 4.4.2pwt <- read_excel("C:/Users/Rishita Chintala/Downloads/pwt100 (1).xlsx",
sheet = "Data")###Filter Data (1990-2020)
data <- pwt %>%
select(country, year, rgdpna, hc, pop) %>%
filter(year >= 1990 & year <= 2020) %>%
mutate(gdp_per_capita = rgdpna / pop,
gdp_growth = (gdp_per_capita - lag(gdp_per_capita)) / lag(gdp_per_capita) * 100)
# Inspect the data
head(data)## # A tibble: 6 × 7
## country year rgdpna hc pop gdp_per_capita gdp_growth
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Aruba 1990 1694. NA 0.0621 27255. NA
## 2 Aruba 1991 1829. NA 0.0646 28299. 3.83
## 3 Aruba 1992 1936. NA 0.0682 28377. 0.276
## 4 Aruba 1993 2078. NA 0.0725 28658. 0.989
## 5 Aruba 1994 2248. NA 0.0767 29313. 2.28
## 6 Aruba 1995 2305. NA 0.0803 28692. -2.12data <- data %>%
filter(!is.na(gdp_growth) & gdp_growth != 0)
data## # A tibble: 5,457 × 7
## country year rgdpna hc pop gdp_per_capita gdp_growth
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Aruba 1991 1829. NA 0.0646 28299. 3.83
## 2 Aruba 1992 1936. NA 0.0682 28377. 0.276
## 3 Aruba 1993 2078. NA 0.0725 28658. 0.989
## 4 Aruba 1994 2248. NA 0.0767 29313. 2.28
## 5 Aruba 1995 2305. NA 0.0803 28692. -2.12
## 6 Aruba 1996 2333. NA 0.0832 28045. -2.25
## 7 Aruba 1997 2516. NA 0.0855 29440. 4.97
## 8 Aruba 1998 2683. NA 0.0873 30746. 4.43
## 9 Aruba 1999 2714. NA 0.0890 30497. -0.809
## 10 Aruba 2000 2837. NA 0.0909 31225. 2.39
## # ℹ 5,447 more rows##Table Output ###Top 5 countries by Average GDP Growth
# Summarize to find average growth per country
country_avg_growth <- data %>%
group_by(country) %>%
summarize(
avg_gdp_growth = mean(gdp_growth, na.rm = TRUE)
) %>%
arrange(desc(avg_gdp_growth)) # Sort by highest growth
# Select the top 5 countries
top_5_countries <- head(country_avg_growth, 5)
# Display the result
top_5_countries## # A tibble: 5 × 2
## country avg_gdp_growth
## <chr> <dbl>
## 1 British Virgin Islands 164.
## 2 Belgium 130.
## 3 Canada 121.
## 4 Saudi Arabia 57.8
## 5 Finland 43.4##Data Visualization ### Average GDP Growth
# Filter data for the top 5 countries
ggplot(top_5_countries, aes(x = reorder(country, -avg_gdp_growth), y = avg_gdp_growth, fill = country)) +
geom_bar(stat = "identity") +
labs(
title = "Top 5 Countries by Average GDP Growth (1990-2020)",
x = "Country",
y = "Average GDP Growth (%)"
) +
theme_minimal() +
theme(legend.position = "none") +
coord_flip() # Optional: Flip for horizontal bars
##Table Output ###Top 5 countries by Human Capital Index
country_hci <- pwt %>%
select(country, year, hc) %>%
group_by(country) %>%
summarize(
avg_hci = mean(hc, na.rm = TRUE)
) %>%
arrange(desc(avg_hci))
# Select the top 5 countries
top_5_hci <- head(country_hci, 5)
top_5_hci## # A tibble: 5 × 2
## country avg_hci
## <chr> <dbl>
## 1 Czech Republic 3.53
## 2 Slovakia 3.45
## 3 Switzerland 3.34
## 4 Slovenia 3.32
## 5 Estonia 3.32##Data Visualization ###Human Capital Index
ggplot(top_5_hci, aes(x = reorder(country, -avg_hci), y = avg_hci, fill = country)) +
geom_bar(stat = "identity") +
labs(
title = "Top 5 Countries by Average Human Capital Index (HCI)",
x = "Country",
y = "Average HCI",
fill = "Country"
) +
theme_minimal() +
theme(legend.position = "none")
##Findings and Conclusion
GDP Growth Trends Countries like Belgium, Canada, and Finland exhibited stable but moderate growth rates, reflecting their maturity as advanced economies. The success of countries like Canada and Finland underlines the importance of diversification and innovation-driven growth.esource-dependent economies like Saudi Arabia and financial hubs like the British Virgin Islands must address volatility risks through structural reforms.Developed nations like Belgium showcase how integration into global trade networks and policy stability drive consistent growth.
Human Capital Index These countries demonstrate that higher HCI strongly correlates with innovation, economic resilience, and higher income levels.Strategic policies focusing on education quality, healthcare accessibility, and skills development play a crucial role in achieving high HCI.Countries like Estonia showcase how integrating technology into education systems can provide a competitive edge in the modern economy.