This tweet by @moorehn (who usually is a superb economic journalist) really bugged me:
Alarming chart of employment for people between 25 and 54. It's like a ski jump. #SOTUecon pic.twitter.com/KNGYmwI88C
— Heidi N. Moore (@moorehn) January 29, 2014
I grabbed the raw data from EPI: (http://www.epi.org/files/2012/data-swa/jobs-data/Employment%20to%20population%20ratio%20(EPOPs).xls) and properly started the graph at 0 for the y-axis and also broke out men & women (since the Excel spreadsheet had the data). It’s a really different picture:
I’m not saying employment is great right now, but it’s nowhere near a “ski jump”. So much for the state of data journalism at the start of 2014.
Here’s the hastily crafted R-code:
library(ggplot2) library(ggthemes) library(reshape2) a <- read.csv("empvyear.csv") b <- melt(a, id.vars="Year") gg <- ggplot(data=b, aes(x=Year, y=value, group=variable)) gg <- gg + geom_line(aes(color=variable)) gg <- gg + ylim(0, 100) gg <- gg + theme_economist() gg <- gg + labs(x="Year", y="Employment as share of population (%)", title="Employment-to-population ratio, age 25–54, 1975–2011") gg <- gg + theme(legend.title = element_blank()) gg
And, here’s the data extracted from the Excel file:
Year,Men,Women 1975,89.0,51.0 1976,89.5,52.9 1977,90.1,54.8 1978,91.0,57.3 1979,91.1,59.0 1980,89.4,60.1 1981,89.0,61.2 1982,86.5,61.2 1983,86.1,62.0 1984,88.4,63.9 1985,88.7,65.3 1986,88.5,66.6 1987,89.0,68.2 1988,89.5,69.3 1989,89.9,70.4 1990,89.1,70.6 1991,87.5,70.1 1992,86.8,70.1 1993,87.0,70.4 1994,87.2,71.5 1995,87.6,72.2 1996,87.9,72.8 1997,88.4,73.5 1998,88.8,73.6 1999,89.0,74.1 2000,89.0,74.2 2001,87.9,73.4 2002,86.6,72.3 2003,85.9,72.0 2004,86.3,71.8 2005,86.9,72.0 2006,87.3,72.5 2007,87.5,72.5 2008,86.0,72.3 2009,81.5,70.2 2010,81,69.3 2011,81.4,69
