The Jobs Report Is Wrong
What headlines say about the monthly jobs numbers is actually no more accurate than chance. A BuzzFeed original analysis.
Months in red are months where initial headlines said the jobs numbers fell short of economists’ expectations, but then the revised numbers actually exceeded expectations (or vice versa). Numbers represent the difference between expected job creation and government reported numbers.
Economists’ expectations were drawn from a Bloomberg survey of economists, and jobs figures were gathered from BLS.gov. Final benchmark revisions for the previous March are released by the Bureau of Labor Statistics in February of the following year.
“Stunning.” “Pleasant surprise.” “Easily beat economists’ predictions.” These are some of the phrases being used to describe February’s employment report, which showed 236,000 new jobs created last month. But a few months or a year from now, there’s a decent chance we’ll have an entirely different perception of today’s news.
The Bureau of Labor Statistics, which releases monthly estimates of job growth based on firm surveys, continually revises these figures as more information streams in. And while the monthly announcements receive the most attention, the revised figures often vary widely from initially reported numbers.
Most jobs-report headlines focus on how the economy performed relative to economists’ expectations. So BuzzFeed looked at initially reported figures, economists’ forecasts, and final benchmark revisions released annually to find out how often the narrative of performance relative to expectations turned out to be wrong.
A full 50% of the time, the initial perception of the jobs numbers would have been incorrect: Reported jobs growth fell short of economists’ expectations, but then the revised numbers actually exceeded them, or vice versa. In other words, if you want to know what today’s jobs report means for the economy, reading the headlines is no more useful than flipping a coin.
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doozer 2 months agoData scientist? The real pattern here is that in almost all cases (July 2011 the exception) there was improvement over the initially stated number. The revision is better. So, while 236K beat estimations, by the patterns here, the revised figure should be even further above that, right? Data scientist? So your 50-50 marker is rather laughable. Your point is twisted and spun. And you failed to find the larger trend…data scientist.
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doozer 2 months agoMan…so much more wrong with this. 1) It’s bls.gov…not bls.org. Nice citation scientist.
2) I’m looking at the bls.gov report here:
and see some very different numbers from ones you present. I don’t know how you square your numbers with these from the BLS, starting April 2011: Apr-11304
May-11115
Jun-11209
Jul-1178
Aug-11132
Sep-11225
Oct-11166
Nov-11174
Dec-11230
Jan-12311
Feb-12271
Mar-12205
Apr-12112
May-12125
Jun-1287
Jul-12153
Aug-12165
Sep-12138
Oct-12160
Nov-12247
Dec-12219
Jan-13119p
Feb-13236p Are you using different numbers than the BLS? Which ones? You certainly don’t cite well, and you are at minimum mixing your metaphors, talking about 236K and then not presenting your tabular data that matches your original figure. I work in data too, and integrity is so very important. This is the most shoddy piece of analysis I’ve ever seen. 3) You don’t list the analyst expectation. You highlight when it missed, but you don’t prove it. This is probably related to whatever numbers you manufactured from point 2. -
doozer 2 months agoThe space didn’t come through. The format for the months is Mon-YY. It should look like this: Apr-11 304
May-11 115
Jun-11 209
Jul-11 78
Aug-11 132
Sep-11 225
Oct-11 166
Nov-11 174
Dec-11 230
Jan-12 311
Feb-12 271
Mar-12 205
Apr-12 112
May-12 125
Jun-12 87
Jul-12 153
Aug-12 165
Sep-12 138
Oct-12 160
Nov-12 247
Dec-12 219
Jan-13 119p
Feb-13 236p
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