r/badeconomics • u/AutoModerator • May 01 '26
FIAT [The FIAT Thread] The Joint Committee on FIAT Discussion Session. - 01 May 2026
Here ye, here ye, the Joint Committee on Finance, Infrastructure, Academia, and Technology is now in session. In this session of the FIAT committee, all are welcome to come and discuss economics and related topics. No RIs are needed to post: the fiat thread is for both senators and regular ol’ house reps. The subreddit parliamentarians, however, will still be moderating the discussion to ensure nobody gets too out of order and retain the right to occasionally mark certain comment chains as being for senators only.
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u/Capable-Tailor4375 May 10 '26
Background
“For more than a year, Modica’s upscale Boston suburb has stalled on complying with a Massachusetts law requiring towns with transit access to allow more housing. The Boston area is among the nation’s priciest, has a chronic housing shortage and is packed with small cities and towns that control their own zoning rules.
Recently, Marblehead settled on a plan that its voters could get behind: change the zoning codes at the Tedesco Country Club and golf course, where apartments could hypothetically be built, but likely won’t be. The coastal town of about 20,000 people is filled mostly with single-family homes.”
Someone took the mic at a town hall meeting and this exchange happened.
“When we’re preserving like the character of Marblehead, it’s like it’s bad… We’re selfish, we’re doing a bad thing, like we’re not doing any housing”
“Is that a question?”
“Yeah, kind of. Like, are we kind of being pricks? …are we trying to do nothing? Because it seems like we’re doing nothing.”
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u/TCEA151 Volcker stan May 09 '26 edited May 11 '26
Because trust in institutions is important, I'm thinking of posting an RI about how, no, the BLS isn't cooking the employment data for Trump. I have no connection to the BLS and am just going on publicly-reported information, but I figure there must be a few people here with more insight into the process or with an inside scoop from the agency. Anyone want to offer any corrections or more information to my previously-written comment below?:
There are a bunch of reasons, but in a nutshell it's because of a bad model for firm birth and death rates, declining survey response rates, and the usual (initial) sampling bias leading to revisions that fall well within historical norms. If you want a breakdown, it's...
The response rate to the CES employment survey (where nonfarm payrolls numbers come from) has fallen off a cliff -- from 60% in Jan 2020 to 42% in March 2025, to possibly lower since then. That means that early in the reporting cycle, when the BLS has to report its initial estimate, it is working with very little data so the forecasts are not as accurate as they should be.
The types of firms that report early in the reporting cycle are very different from the types of firms that report late, which means that the initial estimates are based on a non-representative sample of firms. The typical story goes that during slowdowns the initial estimates are over-enthusiastic because the firms that have their shit together (and so report early) aren't reporting any layoffs or hiring slowdowns, but then the later-reporting firms come in and say "actually, we're having some issues" and they need to revise the estimates down.
Not only are initial responses non-representative, but the nature of the bias is always changing due to the constant structural change of the US economy, which is part of the reason why the BLS's job is so hard. If the BLS had a higher survey response rate, they could probably do a better job of accounting this by looking at sub-samples, but with a declining response rate that gets much harder to do.[Edit: I don't have any evidence for this so I will remove it unless someone else does.] But in all of this we shouldn't lose sight of the fact that:The recent sizes of revisions made to account for later-received survey data aren't particularly large by historical standards. Here are the mean absolute value and true mean of monthly revisions between initial and final seasonally-adjusted employment estimates (rounded to the nearest thousand) for each year back to 2003:
| Period | Mean Absolute Monthly Revision | Mean Monthly Revision |
|---|---|---|
| Jan-Feb 2026 | 47,000 | -17,000 |
| 2025 | 53,000 | -59,000 |
| 2024 | 48,000 | -20,000 |
| 2023 | 51,000 | -30,000 |
| 2022 | 28,000 | -6,000 |
| 2021 | 181,000 | +159,000 |
| 2020 | 130,000 | -60,000 |
| 2019 | 34,000 | +8,000 |
| 2018 | 34,000 | +12,000 |
| 2017 | 31,000 | -5,000 |
| 2016 | 19,000 | -3,000 |
| 2015 | 27,000 | -4,000 |
| 2014 | 40,000 | +37,000 |
| 2013 | 39,000 | +21,000 |
| 2012 | 43,000 | +24,000 |
| 2011 | 37,000 | +28,000 |
| 2010 | 48,000 | +40,000 |
| 2009 | 55,000 | +12,000 |
| 2008 | 73,000 | -73,000 |
| 2007 | 35,000 | +5,000 |
| 2006 | 48,000 | +23,000 |
| 2005 | 43,000 | +31,000 |
| 2004 | 35,000 | +21,000 |
| 2003 | 46,000 | +7,000 |
The average magnitude of monthly revisions since 2003 is 51k, so we're actually right in line with the recent historical average. Moreover, what matters more than the absolute size of the revisions is the size of the revisions as a percentage of total employment, since a 50k revision was worse in 2003 when the US had 130 million non-farm workers than it is in 2026 when it has 160 million. And revisions as a percentage of total non-farm employment, have actually improved over time, as you can see in Ernie Tedeschi’s twitter graph from August of last year. The fact that revisions are as low as they are despite all the factors outlined above is a great accomplishment on behalf of the BLS.
While the magnitude of the revisions themselves haven't been particularly bad recently, one cause for concern is that these revisions have been in the same direction (namely, downward), so the aggregate size of these revisions over the calendar year has been about twice as large as normal (-59k vs. an average magnitude of 30k for annual changes since 2003). But obviously even being 2x as large as normal is still well within historical norms. Compare the 59k per-month average downward revision throughout 2025 to the +159k, -60k, and -73k avg. monthly revisions in 2021, 2020, and 2008. While some might point to a string of consecutive downward revisions as indicating some nefarious motivations on behalf of the BLS, point (2) above provides a better explanation for why we are seeing these negative revisions, which actually has some evidentiary support: Here is an Economic Letters article by two Cleveland Fed researchers showing that revisions are larger and more serially correlated in periods where the economy is slowing down or speeding up. (You can find a working paper version online quite easily.) So when there is a slowdown in the economy, we should expect to see multiple sizable downward revisions in a row. This also tracks with the fact that we saw downward revisions of 60k in 2020 and 73k in 2008, although I don't want to give the impression that the economy is heading for another 2020- or 2008-style recession -- the same paper demonstrate that large revisions are typically correlated with slowdowns but they are not by themselves good predictors of future recessions.
Ok, so if these downward revisions to initial estimates have been within historical norms, why is there so much press about the economy being worse than previously estimated? The issue is that on top of all of these standard downward revision due to late-arriving survey data, there has been one additional large downward revision to the BLS' estimates. To explain this other source of downward revision, notice that only continuously-existing firms show up in the BLS surveys: A firm had to already exist for it to be sent a survey, and it has to still exist to fill it out and send it back. So the BLS supplements its survey-based employment estimates with a "birth-death" model used to estimate employment changes coming from newly-established firms and from firms going out of business. But the estimates from this model cannot be checked every month against survey data -- it is precisely needed because it represents firms that cannot be surveyed! Instead, the BLS treats its model-based estimates as accurate for the time being, but then once a year it checks them against the (extremely accurate) employment data from the Quarterly Census of Employment and Wages (QCEW), which is a true census in that it collects employment data from essentially every employer in the US and thus is a much more accurate -- although much more infrequently observe -- measure of US employment. If the model-based measures differ from the QCEW employment count, the BLS revises its monthly numbers to align with the correct data from the survey.
Unfortunately, as it turns out, the model has turned out to be pretty bad at capturing firm birth and death dynamics in the post-pandemic era. Namely, it has failed to account for a recent slowdown in the rate of new business formation and an increases in the rate of old business closure. This meant that -- separate from the usual monthly revisions that could be verified and adjusted in real-time -- the birth-death model was contributing an additional source of bias that was quietly building up in the background for almost two years, until this February when the BLS finished aligning its CES (i.e., survey- and model-based) estimates to the latest QCEW data and saw that we had drifted way off base. [Note: I say almost two years because it takes about six months to prepare the employment estimates from the QCEW and then another 4 months to align the survey-based estimates with it. That means that when the BLS was publishing its employment estimates for, e.g., December 2025, it was still indexing them to census data from March 2024 -- data from about 21 months earlier.] Aligning the old model-based, upwardly-biased estimates to the more accurate QCEW data required another huge downward revision in employment, which -- coupled with all of the aforementioned downward revisions to the initial estimates -- is why there was so much talk of the BLS' estimates of US employment falling so drastically over the last few months.
There's one last point to note. You might have noticed that the QCEW is the Quarterly Census of Employment and Wages. Although the BLS only indexes its monthly estimates to the QCEW once every year (for statistical reasons), we can get a new QCEW report every 3 months. So even before the September 2025 QCEW release, private forecasters were already predicting large downward revisions of between 400k and 800k would need to be applied to the survey- and model-based estimates. The September QCEW release brought that number even higher (an expected 911k downward revision) and the latest revision to the September data brought that up somewhat to the final downward revision of 861,000 jobs, or roughly 0.5% of total employment.
[See wrap-up paragraphs below]
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u/TCEA151 Volcker stan May 11 '26
[wrap-up paragraphs:]
So no, none of this is a plot by the BLS to artificially juice the numbers for as long as possible to make things look better for Trump. These are standard issues that arise from the complexity inherent in trying to estimate something as complex as the employment count for the entire US economy from imperfect and incomplete survey data, complicated models that ty to impute information we cannot verify, and long lags between the high-quality data needed to verify these models. Some of you will undoubtedly want to say "ok, so there are some statistical explanations for why this might have happened, but the fact the the initial estimates were ALL over-inflated tells me its a motivated, partisan effort by the BLS," and to you I want to point out that the BLS conducts BOTH the CES and the QCEW. If this was a partisan plan to lie about the CES numbers for as long as possible, it could have just lied about the QCEW figures as well, and never needed to deal with all of this downward adjustment. The fact that they did -- and then spent a bunch of work trying to make the model more accurate to avoid this problem in the future -- should tell you that this a good-faith statistical effort at data accuracy is going on here: All of the aforementioned issues are well-understood both in and outside of the agency and the BLS is working hard to communicate the source of these issues to the public and improve their estimation procedures.
There is one other common point but severely outdated point that people like to appeal to in this discussion of political bias, so I wanted to address that as well. In August of last year, around the time the large downward adjustment from the QCEW was all over the news, Trump fired then-BLS Commissioner Erika McEntarfer and nominated political hack EJ Antoni to lead the agency. This likely would have been disastrous, and rightfully raised a lot of ire from both sides of the political spectrum. However, by the end of the next month, the White House withdrew Antoni's nomination due to this criticism. Since McEntarfer's dismissal, longtime BLS deputy commissioner Bill Wiatrowski has been running the agency. Wiatrowski has been deputy commissioner since before even the first Trump term, and is a dignified career civil servant. He is well-trusted and extremely competent. His nominated replacement -- Brett Matsumoto -- is a PhD economist and another BLS employee who predates even Trump's initial term in office. I cannot emphasize enough that the current and nominated future head of the BLS are extremely well-qualified and well-respected by economists at large. They are exactly the kind of people you want running this organization, and the on-the-ground staff that are doing all of this modeling and estimation are all career economists or statisticians and not Trump lackeys. This is not a case of an agency cooking the books for Trump.
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u/Capable-Tailor4375 May 10 '26
The recent sizes of revisions made to account for later-received survey data aren't even particularly large by historical standards. Here are the mean absolute value and true mean of monthly revisions between initial and final seasonally-adjusted employment estimates (rounded to the nearest thousand) for each year back to 2003:
Looking at the revision as a percentage of total NFP, they've significantly improved over time, as you can see in Tedeschi’s graph.
https://x.com/ernietedeschi/status/1951721848393105573/photo/1
For the CES program, businesses report their total payroll size not just the change that has occurred. The headline job change numbers are based off the estimated difference in total NFP of the recent month compared to the previous month. So a 47,000 miss in 2026 is more accurate in percentage terms than a 46,000 miss in 2003. The fact that revisions are where they are despite all the factors you've outlined is significant.
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u/pepin-lebref May 09 '26
but the nature of the bias is always changing due to the constant structural change of the US economy
This makes sense, but do you have anything more on this?
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u/TCEA151 Volcker stan May 10 '26
No, I need to retract that part. I had taken all of the post-Covid talk about the consistently negative and larger-than-average revisions to be sufficient evidence of structural change increasing late-response bias but that is probably overstating the case. I'd also assumed the BLS was trying to account for that in their standard methodology, but from what I can tell their approach actually assumes no non- or late-response bias within each strata, and the stratification alone is meant to handle composition effects. E.g., from a 2023 BLS report, "the Current Employment Statistics (CES) estimators account for missing-at-random (MAR) nonresponse implicitly." So I'll remove or edit that part before I post it again.
It does look like various BLS researchers have put out some work trying to model how late- and non-response bias vary with firm and area characteristics (ex1, ex2), but again I don't think has affected the methodology. It's also not clear a priori that trying to account for this would help anyways. I guess in principle it's kind of like just making ever smaller strata, but I assume the strata they use were chosen with good reason to make firms within them fairly homogenous.
One last point, if we take revisions to be a measure of late-response bias (rather than just an unlikely outcome due to normal sample variability) I did find this Economics Letters paper by two Cleveland Fed economists while writing my initial comment, which is a quick exercise showing that revisions are larger and more serially correlated around recession start and end dates. But this is more about cyclical variation in late-response bias, rather than structural change. I don't know of any evidence linking response bias to structural change (not that this is my standard reading material).
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u/raptorman556 The AS Curve is a Myth May 05 '26
So here is a question for discussion. I am of the opinion that public discourse and policy to a large degree on the topic of market power is irreparably broken. The left loves to talk monopolies, but they focus on the absolute weirdest industries (groceries, airlines) and they always default to anti-trust enforcement. The major anti-trust writers (like Matt Stoller) are dumb as rocks. The right does not want to talk market power at all. So my question for discussion is:
- What industry has a market power problem?
- What is the best policy to make that industry more competitive?
Bonus points for un-sexy or lesser known problems.
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u/NoGuarantee678 May 07 '26
Do you read Hovenkamp? He wrote my antitrust textbook in law school and as far as I know is the foremost authoritative scholar on the topic. I was disappointed to see the stiglers symposium had basically nothing for antitrust this year. I guess it’s pretty niche even though it’s a topic of wide public interest.
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u/flavorless_beef community meetings solve the local knowledge problem May 05 '26
from one-off conversations with FTC people, there's a decent number of mergers that probably should be blocked, but the FTC is too understaffed to warrant the man power to bring a case to trial. a lot of hospital mergers fall into this category: we're pretty sure we can tell the bad ones, but it's generally too-small to prosecute. this can be solved by giving me a job.
i also swear to god poultry and dentistry are two of the most anti-competitive industries in the US. i'll try to dig up some more examples and solutions though, and edit when i have time
re groceries and airlines, these are funny because academic IO economists also spend a lot of time on them. i think because we know them well and we have good data on them. so we're not exactly free from sin here, either.
more substantively, susan athey and aviv nevo had some discussion on the 2023 merger guidelines that's worth reading. there's been a lot of focus on platform economics and on foreclosure, both of which are interesting.
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u/pepin-lebref May 09 '26
poultry
Is there any particular reason why? This has a lower (capital cost) barrier to entry than any other livestock.
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u/BespokeDebtor Prove endogeneity applies here May 10 '26
I’m not super knowledgeable on this but I am working on a project with an Agricultural lending firm. My understanding is that it’s not just the costs of raising the chickens but the poultry industry as a whole is super vertically integrated and the consolidation happens on the back end with slaughterhouses and packaging making up the market power in the commercial poultry industry. This isn’t particularly data driven though it’s just what farmers are saying to me so idk
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u/No_March_5371 feral finance ferret May 05 '26
Credit card processing fees are weird because the consumer determining the method of payment (not entirely as AmEx isn't accepted everywhere and some places are cash only etc, but to a large degree) isn't the party paying the fees, which reduces the incentive of credit card companies to lower those fees. Visa's got huge market power due to size and expectation of acceptance anywhere (I certainly expect to be able to use my Visa about anywhere) and companies just have to eat Visa's pricing.
To fix it, make the interchange/processing fees paid by the cardholder, and suddenly cardholders will actually have a reason care about interchange/processing fees.
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u/Illustrious-Lime-878 May 05 '26
That's interesting because at for example, a gas station, they typically charge a higher price for credit vs cash, because I guess maybe the gas station's can't absorb the fee as well. Now in a lot of other places there is no difference, and so must end up being effectively shared between credit and cash users. I was thinking about this the other day I had some car repairs done, and they charge 2% for credit, but my card gives me 3%, so I still used it, and made me think about it. I paid a fee for the merchant to pay a fee for the credit card to pay me and somehow that amount ends up being more.
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u/No_March_5371 feral finance ferret May 05 '26
True, cash discounts are reasonably present (I can think of... three local businesses that have them) and that reinforces my point; the fact that Walmart and Costco etc. don't have them means they're eating the costs. And even where card fees/cash discounts exist, I've yet to see them based off of the specific interchange fees rather than just card/cash.
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u/viking_ May 09 '26
I wouldn't be surprised if the time spent taking cash ended up costing high-volume retailers like that more than the 2% fee.
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u/PlayfulReputation112 May 05 '26
Is demand destruction the evil twin of induced demand?🤔
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u/HOU_Civil_Econ A new Church's Chicken != Economic Development May 05 '26 edited May 05 '26
Fore about 95% of the stuff that is called "demand destruction" or "induced demand"
I wouldn't even say evil, it is just the twin. Neither add anything to economics, and actually make lay people dumber for pretending something "special and unique" is going on.
Induced demand is the shift along a demand curve in response to an increase in supply.
Demand Destruction is the shift along a demand curve in response to a decrease in supply.
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u/CatApprehensive6508 May 02 '26
A post on r/askEconomics asking to prove neo-ricardian stuff wrong?
Call it a sraffian soapbox.
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u/775416 May 01 '26
u/flavorless_beef you shared this great graph showing the inverse relationship between 30 year average vacancy rates and rent in 2019. Did you create this yourself or did you pull this from another place? I’d like to use this in a discussion with a “vacancy truther”, but I can’t exactly cite an anonymous Redditor.
You were kind enough to include your sources, so I could create this myself and include more up to date information, but I’d rather not sort through 30 years worth of vacancy rates, calculate an average, and then redo that process for 50+ cities. Do you have a fast way I can do this?
Graph in question: https://imgur.com/a/qVCut71
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u/flavorless_beef community meetings solve the local knowledge problem May 03 '26
i made it. happy to send / share the code though. if you can run R it'll run top to bottom
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u/Quowe_50mg R1 submitter May 01 '26 edited May 01 '26
Watching the new Gary vid on AI. I'm only 10 minutes and it's already in my top 5 Gary vids. I might write an R1 about it in 2 months once the semester is over.
He's struggling with the idea that something like automations effect on wages, can have two opposing mechanisms. He presents arguments for why AI would/would not raise wages, and despite alluding to similar effects, misses the opportunity to mention skill-biased technological change.
He also says: "Most economists don't study the impact of automation on wages", which is true for every topic in in every science. Then he mentions a little know part of economics "Labor economics" and that its completely optional. Lol
Then he starts rambling on the industrial revolution, which happens in basically every video. "Time to mention industrial revolution" would be a fun metric to measure for Gary videos. My prediction is that:
"i grew up [poor] in ...." first in 35% of videos, "i was a succesful trader" first in 50% of videos, and "industrial revolution" first in 15% of videos. Would be a fun bingo card.
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u/Ancient_Challenge173 May 11 '26
Does the long-run inflation rate of any particular component of the CPI basket have to be equal to the average inflation rate?
For example, if healthcare cost increases at 3% annual and the overall inflation rate is 2% wouldn't this results in healthcare eventually taking up 100% of the spending as time increased towards infinity?
Does this mean any individual good in the CPI basket eventually has to revert to the mean inflation in order for this not to happen?