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Job losses in recessions visualized

Recently this graphic was posted on the “The Gavel” blog of the Speaker of the House (What 3.6 Million Jobs Lost Over 13 Months Looks Like, by “Karina,” February 6th, 2009). It shows the number of jobs lost (and recovered) in the recessions of 1990, 2001, and 2008. By juxtaposing the three time periods over each other starting with the peak job month and showing employment change by month, it gives a startling comparison that highlights the severity of the current situation. It also implies that we will have a long time to wait before we reach our previous peak month.

I was curious about this graph and did a little follow up that I share below. Data librarians may find this a bit tedious, but for those who have never used raw data, it may be useful as an illustration of the difference between “data” (the raw numbers that you put into statistical software) and “statistics” (the human-viewable tables and graphs that we see in publications).

Unfortunately, as is often the case with statistical information, the source given for the graphic is incomplete: simply “Bureau of Labor Statistics.” I could not find the graphic itself on the bls.gov site, so I assume that the chart was constructed from BLS data, specifically, the Current Population Survey or the Current Employment Statistics Survey. These two surveys count employment differently — one is a survey of individuals and the other is a survey of employers.

There is a similar, but not identical, chart (“Percent change in total nonfarm employment, from beginning of recession) in the January 2009 (released February 6, 2009) Current Employment Statistics Highlights, Monthly (Bureau of Labor Statistics), so my guess is that someone at the Speaker’s office built the chart from the raw CES data.

Just out of curiosity, I went to the CES “Most Requested Statistics webpage and downloaded “Total Nonfarm Employment – CES0000000001″ for 1990 through the end of 2008. Raw data suitable for analysis even look “raw,” not even like a statistical table:

1990,Jan,109151
1990,Feb,109396
1990,Mar,109611
1990,Apr,109651
1990,May,109800
1990,Jun,109817
1990,Jul,109775
1990,Aug,109567
1990,Sep,109485
1990,Oct,109324
1990,Nov,109180
1990,Dec,109120
1991,Jan,109001
1991,Feb,108695
1991,Mar,108535
1991,Apr,108324
1991,May,108196
1991,Jun,108283
...

Of course, it is relatively easy, using statistical software, to construct tables and graphs from raw data. Here, for example, is a published statistical table with essentially the same raw information (but from CPS, not CES) that I downloaded. (See the full table from Employment from the BLS household and payroll surveys: summary of recent trends, February 6, 2009).

By using the raw data to create a graph, one can tell a story that has more impact than just a table of numbers. It is relatively easy to get these data into statistical software. I used Excel and Stata to create a small time-series data file. I organized it by month (from month “1″ to month “48″) with each row of the data file having data for 3 recessions. The first row has data for the first month of the three recessions, the second row has data for the second month, etc. The CES data has employment totals in millions. For example, the employment for 2008:

 
138152 
138080 
137936 
137814 
137654 
137517 
137356 
137228 
137053 
136732 
136352 
135755 
135178 

I had to compute a new variable for each recession: the cumulative number of jobs lost. So, for example, 2008:

138152	 0
138080	-72
137936	-216
137814	-338
137654	-498
137517	-635
137356	-796
137228	-924
137053	-1099
136732	-1420
136352	-1800
135755	-2397
135178	-2974

The first 12 months with all three recessions (v1, v2, v3) and the computed variables (1990, 2001, 2008) look like this:

month   v1  1990    v2      2001    v3       2008
1   109817    0     132530    0     138152   0
2   109775  -42     132500  -30     138080  -72
3   109567  -250    132219  -311    137936  -216
4   109485  -332    132175  -355    137814  -338
5   109324  -493    132047  -483    137654  -498
6   109180  -637    131922  -608    137517  -635
7   109120  -697    131762  -768    137356  -796
8   109001  -816    131518  -1012   137228  -924
9   108695  -1122   131193  -1337   137053  -1099
10  108535  -1282   130901  -1629   136732  -1420
11  108324  -1493   130723  -1807   136352  -1800
12  108196  -1621   130591  -1939   135755  -2397

Here is a complete tab-separated-values version of the data file I constructed. Then I used Stata to build a graph and it looks very much like the one at the Speaker’s Blog.


Of course, when one tells one story, one leaves out other stories. This graphic doesn’t show that the starting points of the recessions were different:
1990: 109 million
2001: 132 million
2008: 138 million

Open re-usable government information

One could use the raw data to tell a lot of different stories and analyze the data in many different ways. And that brings me to the connection between all this and why we need to be sure that government information is not just “free as in beer” but also “free as in open.”

It is important for statistical agencies to publish statistics to help us understand their raw data. But, it is also essential that they provide us with the raw data so that we can better understand their statistics and do our own analyses. Most of the statistical agencies of the U.S. government do an excellent job of making their raw data easily available. In fact, the rest of government would do well to use statistical agencies as a model for instantiating their information in usable and re-usable formats (in addition to any publishing and presentation of their data/information) so that the information, whether it is text or images or video or sound or numbers, can be used, reused, analyzed, stored, and preserved.

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4 Comments

  1. jajacobs says:

    Here is a p.s. to the job loss statistics above.

    Professor Richard Ashley, Department of Economics, College of Science, Virginia Tech, Blacksburg, has put this little table online for his class, Economics 3204 Macroeconomic Theory:

    It shows the consequences every 10% increase in the number of people unemployed.

    Thanks to Stuart Basefsky!

  2. jrjacobs says:

    Thanks for tracking on this Jim. Those figures sure put a human face on the meaning of unemployment.

  3. jajacobs says:

    Today’s NY Times has a graphic showing job losses in recent downturns (similar to the above graphic — but with more six downturns, job gains and losses, and unemployment rate.

    Job Losses Hint at Vast Remaking of Economy, By PETER S. GOODMAN and JACK HEALY, New York Times, March 6, 2009. And here is a direct link to the graphic.

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