Country-Specific Minimum Wage Data, Courtesy of Wikipedia

I was looking for some data on minimum wages in various countries and found that Wikipedia (perhaps unsurprisingly) has a very nice, well-annotated table. After downloading the data & cleaning it a bit (harder than it should be), I made several plots. There were too many countries for one plot, so I made one for each (approximate) quartile. At the end of the blog post is the R code I used for fetching the data & making the plots.

Fourth Quartile 

NB: Some countries have exemption policies for worker or occupation characteristics, so for a more complete understanding, of say, why Australia appears to have a minimum wage more than 2x the US minimum wage, check the Wikipedia table. 

Third Quartile 

Second Quartile 

First Quartile 

Distribution of Minimum Wages

Below is a some R code for grabbing the table of country-specific minimum wages from Wikipedia.

	############################################################################
	# AUTHOR: John Horton
	# PURPOSE: Extract by-country minimum hourly wages from the Wikipedia page
	# LAST MODIFIED: May 22, 2013
	############################################################################
	library(XML)
	library(ggplot2)
	library(scales)
	url <- "http://en.wikipedia.org/wiki/List_of_minimum_wages_by_country"
	raw <- readHTMLTable(doc=url)
	df.raw <- raw[[2]]
	colnames(df.raw) <- c("country", "minimum_wage", "annual", "annual_ppp", "workweek",
	"hourly_usd", "hourly_intl", "perc_2011_gdp",
	"effective")
	Clean.Wage <- Vectorize(function(x){
	"There is some HTML junk & idiosyncracies in the wages that this fixed"
	x1 <- gsub("US$", "", x) # some have US$ prefix
	x2 <- substring(x1, first = 20) #get rid of span meta-data that XML picks up
	as.numeric(x2)
	})
	Clean.Country <- Vectorize(function(x) {
	"Country has a '_' appended to it--this strips it out"
	substring(x, first = 2)
	})
	df <- with(df.raw, data.frame(country = Clean.Country(country), min.wage = Clean.Wage(hourly_usd)))
	Make.MW.plot <- function(df, label){
	"This makes a minimum wage plot per country based on a passed data frame.
	There are a large number of countries, so the idea here is to split the
	data into subsets & plot them individually.
	"
	title = paste("Hourly minimum wages by country \n", label, sep = "")
	qplot(country, min.wage, data = df) +
	ylab("Hourly Wage (USD) \n \n Source: Wikipedia, May, 21, 2013
	en.wikipedia.org/wiki/List_of_minimum_wages_by_country") +
	xlab("") +
	scale_y_continuous(labels = dollar) + coord_flip() +
	theme_bw() + expand_limits(y = 0) +
	ggtitle(title)
	}
	# Split countries into quartiles
	## > summary(df$min.wage)
	## Min. 1st Qu. Median Mean 3rd Qu. Max.
	## 0.030 0.490 1.180 2.308 2.460 16.450
	g.75 <- Make.MW.plot(subset(df, min.wage > 2.5), label = "> 2.50")
	g.50 <- Make.MW.plot(subset(df, min.wage > 1.25 & min.wage <= 2.50), label = "1.25 < Minimum Wage <= 2.50")
	g.25 <- Make.MW.plot(subset(df, min.wage > 0.50 & min.wage <= 1.25), label = "0.50 < Minimum Wage <= 1.25")
	g.0 <- Make.MW.plot(subset(df, min.wage <= 0.50), label = "Minimum Wage <= 0.50")
	Write.Image <- function(filename, g, width = 500, height = 500, format = "png"){
	"Writes a passed ggplot, g, to the filename. The default format is png."
	do.call(format, list(filename, width, height))
	print(g)
	dev.off()
	}
	Write.Image("./minimum_wage_plots/quartile_75.png", g.75)
	Write.Image("./minimum_wage_plots/quartile_50.png", g.50)
	Write.Image("./minimum_wage_plots/quartile_25.png", g.25)
	Write.Image("./minimum_wage_plots/quartile_0.png", g.0)
	# Bonus plot - kernel density estimate of distribution of hourly minimums
	g.distro <- qplot(min.wage, geom="density", data = df) +
	scale_x_log10(labels = dollar) +
	xlab("Hourly minimum wages in USD, log scale \n Source: Wikipedia, May, 21, 2013
	en.wikipedia.org/wiki/List_of_minimum_wages_by_country") +
	theme_bw()
	Write.Image("./minimum_wage_plots/distr.png", g.distro)

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