FLS Activity Larobis 12338605

FLS Acitivity Codes

# Loaded data
pwt <- read.csv("D:/RStudio/Iloveecon/pwt110.csv")
CPI22 <- read.csv("D:/RStudio/Iloveecon/CPI.csv", header=FALSE)

# Since V2 is ISO3, since V4 = CPI, Keep only ISO3 and CPI
cpi_sub <- CPI22[, c(2, 4)]          

# Since V2 is ISO3, since V4 = CPI
names(cpi_sub) <- c("ISO3", "CPI2022")

# Filter for 2022 only
pwt_2022 <- subset(pwt, year == 2022)

# Compute GDP per capita using GDP and Population (rgdpe and pop) in pwt
pwt_2022$gdp_pc <- (pwt_2022$rgdpe) / (pwt_2022$pop)

# Keep only ISO3 and GDP per capita
pwt_sub <- pwt_2022[, c("countrycode", "gdp_pc")]
names(pwt_sub)[names(pwt_sub) == "countrycode"] <- "ISO3"

# Merge CPI and GDP datasets
CPI_AND_LOGGDP <- merge(cpi_sub, pwt_sub, by = "ISO3", all = FALSE)

# Compute log GDP per capita
CPI_AND_LOGGDP$log_GDPPC <- log(CPI_AND_LOGGDP$gdp_pc)

# Check CPI_AND_LOGGDP dataset
head(CPI_AND_LOGGDP)

# Regression
lm(log_GDPPC ~ CPI2022, data = CPI_AND_LOGGDP)

# Plot
plot(CPI_AND_LOGGDP$CPI2022, CPI_AND_LOGGDP$log_GDPPC,
     xlab = "Corruption Perception Index 2022",
     ylab = "Log GDP per capita 2022",
     cex = 1.2,
     cex.lab = 1.5)

abline(lm(log_GDPPC ~ CPI2022, data = CPI_AND_LOGGDP), col = "red", lwd = 2)