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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Data 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.
Man…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
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.
The space didn’t come through. The format for the months is Mon-YY. It should look like this: Apr-11 304