I’m not sure how the politics play out but a stat that is always off in the same direction, always over and never under, is what some statisticians call “biased.”

You can have non-biased indicators that have error with mean 0.

Maybe a better question, when judging current operations, is how precise the biased estimates are becoming overtime. Is the size of the error increasing or decreasing.

You seem to be under the impression that these figures are exclusively sourced from employers, but they are not. They are sourced in part from a survey of 60,000 households every month, where each household in the the survey for several consecutive months. Here is some information about non-response rate: https://www.bls.gov/cps/methods/response_rates.htm

> you fix incentives and hire competent data gatherers

You assume the data gatherers were at fault... <chuckle>

These data gatherers work for their government. How do you ensure they're happy to gather and publish data which is essentially critical of that very government?

I agree, if you can't publish a number that's pretty accurate then you shouldn't publish anything. That's why i wasn't broken hearted when Trump fired that BLS person. If you can't even get close then someone needs to be found that can or at least has the balls to say "idk what the number is, we're not publishing without good data".

In fact, the BLS budget has been cut (DOGEd) and they have been warning for months now that this is impacting their ability to follow up on surveys.

The downward revisions were overall a lot bigger than the upward revisions. BLS methodology causes it to get data from larger companies first, and this tends to systemically bias it towards downward revisions

I believe you may correct; the final point is still absent (since the link chart predates the data in the story) and would (I think) continue the downward trend, but by how much is not clear.

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...

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.

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

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

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

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.

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.

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.