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georgeecollins 3 days ago

The department of labor has a methodology that gives an early number that tends to get revised. They have been doing it the same way for a long time and it would not be controversial, except the business press with too little news to report makes a big deal about it with very little context. So its, like jobs increased 200k (not saying +/- 200k is the error bar). It would be good for everyone if people who are not professional economists paid less attention to this.

logifail 3 days ago | parent | next [-]

> It would be good for everyone if people who are not professional economists paid less attention to this.

It would be good for everyone if the BLS figures were trusted.

Even "not professional economists" might lose trust in figures which are regularly revised downwards ... months after being published.

DavidPeiffer 3 days ago | parent | next [-]

It's the best data that's available at the time. It's been collected in a consistent fashion for a significant duration of time. To understand how a new methodology would differ, we'd want to run a new process in parallel for a couple years.

A few notes from an interview on the Odd Lots podcast, interviewing Bill Beach, former head of the BLS:

* Response rates among surveyed employees are roughly:

Month 1 68%

Month 2 83%

Month 3 93-94%

* Large employers tend to respond sooner, and are staffed to handle these requests better.

--------

April 2025 interview: https://podcasts.apple.com/us/podcast/some-of-americas-most-...

August 2025 interview (after BLS head statistician was fired): https://podcasts.apple.com/us/podcast/bill-beach-on-how-trum...

Some notes and a transcript: https://www.crisesnotes.com/bloomberg-odd-lots-podcast-trans...

babblingdweeb 3 days ago | parent | prev | next [-]

Trust in the numbers is extremely important and we should all be calling out that we need to trust the numbers and the methodology. It should also be transparent.

However, it's extremely common in forecasting to revise the forecast once actuals come in. In the case of the BLS, it's the documented approach for a very long time.

Every month the numbers are adjusted and annually. All of the notes as to why, the method, etc are in the actual reports*.

*I don't recommend reading them or the footnotes unless you have insomnia. :)

** Also, if the source data is inaccurate, corrupted, etc; if the models are non-transparently adjusted, that would be horrible and cause for alarm. At the moment, we don't know if that is the case. Yet.

albumen 3 days ago | parent | prev | next [-]

As posted just a few posts above yours, this link provides some helpful factual reference: https://www.statista.com/chart/34931/difference-between-prel...

michaelbuckbee 3 days ago | parent | prev | next [-]

The stats are "regularly revised" (sometimes up, sometimes down).

Though honestly, I wish the terminology were changed to "forecasted" and "actual" to be clearer.

SpicyLemonZest 3 days ago | parent | prev | next [-]

They're preliminary figures! The only alternative is for BLS to be less transparent, collecting data and then refusing to release it until they have enough information to construct their final estimate.

Hamuko 3 days ago | parent [-]

I feel like there's a real alternative in changing your methology for estimating job creation, especially as survey response rates continue to fall.

mandevil 3 days ago | parent [-]

Yes, and both William Beach (the one confirmed under Trump in 2019 who served through most of Biden's term) and Erika McEntarfer (the one confirmed under Biden who was just fired by Trump) talked about how they wanted to improve the methodology.

Because these numbers are so important- to journalists, to the Fed, to financial markets, etc. they wanted a few million dollars extra, over a few year period, to run the new methodology and the old methodology side-by-side for a significant portion of a business cycle, to understand the differences before they switched, and to gain confidence in the system. Because an important part of this particular data set is what it signals to those others, it is important not to move quickly with this data set, but to give time for everyone to understand all the nuances. It's things like, how the market views the meaning of corrections would be different under a different system, and so they want time so that they themselves and all those other people whose jobs depend on understanding it to be fully aware.

Basically, they wanted to run a blue-green deployment strategy for their updates, but couldn't get the budget for it- and their budget has instead been cut so far. So they have prioritized continuing the system that everyone understands rather than experimenting with new things that no one understands. Because these are smart, well educated people who spend their entire lives thinking about these problems, and understand how the data is used, this is something they have thought about a lot and want to do the best job they can.

kgermino 3 days ago | parent | prev [-]

they're regularly revised up or down because they're (very openly) preliminary numbers released just days after the month ends and before many employers have even answered the survey. When the economy reaches an inflection point they tend to be streaky (multiple revisions down or up in a row) but that's nothing new and mostly just means that the economy has been getting worse over the last year and a half, which... that's one of the big arguments for Trump's victory so I'm not sure why it would be a surprise.

triceratops 3 days ago | parent | prev [-]

Serious question: employers presumably also use DoL numbers to decide whether to invest or cut. Could incorrect initial numbers lead to a self-fulfilling spiral?

bluGill 3 days ago | parent [-]

Employers use a lot of data. DoL numbers are one, but they are trying to predict their own future needs. They generally have much better sources of data to their industry that are used as well.

Different companies react differently as well. Companies that have a steady flow of cash (food is very inelastic - people eat about the same every day) realize they can give smaller raises, and this is a good time to invest in the company by building so they often hire. Companies that make luxury goods for the common man (think small boats - large yachts for the rich are different) tighten their belts because they are the first place people in fear cut spending.