Below are five data graphics from my new book An Illustrated Guide to Income in the United States (pgs 106, 108, 109, 110, 112) that shows the long-term growth in wages in the US.
Click on the images for a closer look. This will open a lightbox the same size as the browser window.
Over the last couple of centuries there has been a steady increase in wages for both unskilled workers...
....and production workers. A lot of this growth is a result of the increases in worker productivity due the industrial revolution of the late 1770s and 1800s. However, over the last 40 years, this long-term growth has stopped or slowed down...
Updated 3/25/13: This Production Workers series includes benefits all the way back to when benefits became measurable in the early 1900s. More detailed definition can be found here.
...even though the GDP per person continues to grow. At the same time, the growth rate of GDP per worker has slowed compared to the overall growth of the economy.
Looking at just goods-producing industries, wages dropped for manufacturing, construction, and mining & logging since their a peak in the 1970s.
But among so-called service industries over the same time period there has been either a dip in wages or no real growth. Exceptions include jobs in the financial industry, education & health services and "other" services (which is mashup of occupations like auto mechanics, pet care, promoting political causes or religious activities).
Data Sources for Wages (See bibliography for more references)
Officer, Lawrence H., and Samuel H. Williamson. “Annual Wages in the United States, 1774–Present.” MeasuringWorth.com, 2011. http://www.measuringworth.com/uswage/.
US Bureau of Labor Statistics. “Table B-8. Average hourly and weekly earnings of production and nonsupervisory employees on private nonfarm payrolls by industry sector, seasonally adjusted.” November 2012. http://www.bls.gov/webapps/legacy/cesbtab8.htm.
Designer's Notes: Some of my thoughts on the design and the approaches I used.
One of the many data series that have exponential growth. They are a challenge since I want to make the graph accessible to a wide (non-finance) audience but log graphs are very helpful when you need to see the relative changes across the entire time series.
Through out the book you will see me stacking two or more graphs when they have same timeline on the x-axies. EIther I want to facility comparisons of different time series or in this case show the same data on a "normal" y-axis and a log scale. You may also notice the absence of the GDP per person on this graph. SInce I was working with average hourly wages over 200-year period I didn't feel comfortable converting it to an annual salary for production workers who can often work irregular hours.
Unlike the previous two pages, values of the salaries was not the focus of this graphic. In this case I wanted to compare the change over time of GDP per person vs GDP per worker so I converted the series so they both started from the same point in 1947 (the first year I had the data for the civilian labor force).
When looking at the historical wages for the major industries, I tried several ways to group the series hoping I could show some interesting patterns. I finally went with the simplest approach and created one graph for goods-producing industries...
...and a second graph for service-providing industries. While this worked well for the first graph, 3 time series with similar behavior, with the service graph I was left with a less optimal design, a "spaghetti graph", which I normally try to avoid. I decided to keep scale the same on both graphs to show the overall pattern for the goods and service industries rather than splitting each series into separate smaller line charts.
In each case, the original graph was created in OmniGraphSketcher. Additional annotations were added in Illustrator and when multiple graphs are need they are laid out in Illustrator. Finally, I link to the Illustrator file from within an InDesign document where i add page titles, a footer for data sources and pagination.