CHART

SHOWING RATE AND MONTHLY CHANGE FROM 1948 TO 2020 OF SEASONALLY ADJUSTED

UNEMPLOYMENT IN THE UNITED STATES

April 2020

+234%

FOR THOSE 16 YEARS AND OVER

Compiled from data as reported by the Bureau of Labor Statistics

15%

EXPLANATION. Each circle represents the absolute percentage change compared to the previous month. Larger circles mean greater change. Shaded circles indicate greatest increase since previous greatest. Circles are placed by time horizontally and by unemployment rate vertically.

10%

Oct. 1949

+20%

Dec. 1953

+29%

May 1949

+15%

5%

Feb. 1948

+12%

0%

January 1948

May 2020

CHART

SHOWING RATE AND MONTHLY CHANGE FROM 1948 TO 2020 OF SEASONALLY ADJUSTED

UNEMPLOYMENT IN THE UNITED STATES

FOR THOSE 16 YEARS AND OVER

Compiled from data as reported by the Bureau of Labor Statistics

0%

5%

10%

15%

Jan.

1948

Feb. 1948

+12%

May 1949

+15%

Oct. 1949

+20%

Dec. 1953

+20%

April 2020

+234%

May

2020

EXPLANATION. Each circle represents the absolute percentage change compared to the previous month. Larger circles mean greater change. Shaded circles indicate greatest increase since previous greatest. Circles are placed by time horizontally and by unemployment rate vertically.

In April 2020, the Bureau of Labor Statistics estimated a national unemployment rate of 14.7%. It wasn’t just the rate itself but how fast it spiked. The month before it was at 4.4%, so as businesses shut down, the rate more than tripled in just a month.

Looking at rates since 1948, for which BLS provides monthly estimates, the biggest single-month jump before the pandemic was during the recession of 1953. In December of that year, the rate increased by 29%.

Become a member. Support an independent site. Make great charts.

Join Now

Learn to Visualize Data See All →

Working with Line Maps, the Google Places API, and R

A frequent challenge of visualization is behind the scenes, to get the data and to mold it into the format you need. Do that. Then map.

Using Transparency in R to Improve Clarity

When you plot a lot of data at once, points and lines can obscure others and hide patterns. Transparency can help reveal what is really there.

How to Visualize Proportions in R

There are many ways to show parts of a whole. Here are quick one-liners for the more common ones.

How I Made That: Animated Difference Charts in R

A combination of a bivariate area chart, animation, and a population pyramid, with a sprinkling of detail and annotation.