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I remember other discussions on this forum. Inflation and unemployment data. Long arguments about not trusting the economic data. This is why. These figures are totally arbitrary. There is no neutral competent adults-in-the-room authority anymore. Everything is this bad.
I have a friend who used to work at the Fed. He says to the extent that the figures weren't made up, they hsve basically no basis to reality. The numbers they report just reflect the process of the people creating them, which is bureaucratic and dull.
It's not so much that they are arbitrary, but interpreting them is hard, subjective, or imprecise.
I think a lot of this is down to incentives. Nobody wants to be the government that gives a bad economic report. You don’t want to be up for election when crime is up or unemployment is high or inflation is high. So there’s a lot of pressure on the agencies making these reports because your boss is a political appointee and making his elected boss look bad is going to hurt his career. As such, people are using formulas that are inaccurate and almost always in the direction of making the boss look as good as you can get away with. Which is pretty easy when you can change the formula to suit the purpose. Unemployment rates are hot garbage because basically it’s only counting people actively seeking work within a 3 month timeframe. Which means that if you’re not actively filling out applications, you’re not unemployed. This obviously doesn’t count people who are discouraged or retraining because their old skills became useless. U6 is more useful because it counts all workers available, and if anything overcounts as a full time student is counted even if he doesn’t want a job at all.
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They're not arbitrary. In the case of inflation and unemployment they follow long-established procedures which have the advantage of being consistent if not always accurate. The effects which have led to recent significant downward adjustments in jobs figures have been known for a long time and taken into account by those who care (i.e. not the media). The FBI figures are a different case, because they have been "modernizing" their methodology, leading to instability in the numbers. As I recall, the original release included a warning that many major jurisdictions weren't included, so the revision is no surprise.
The procedures are arbitrary and usually hand large amounts of discretion to workers who are trying to parse complicated realities. If it wasn't complicated, we wouldn't see these wild swings in the data. You'd just count up everything and run some averages.
I'm fairly sure employment, unemployment, and inflation figures do NOT hand large amounts of discretion of workers at all. The wild swings are for other reasons. Late data coming in. Models used to estimate missing data being consistently wrong in certain cases. That sort of thing.
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This is just intellectual cowardice. Ignoring for a second that this particular controversy is a total nothingburger (see my reply to @gattsuru), statistics which are in some sense 'constructed' are the only way of understanding any large scale and complex societal phenomena, whether it be crime, inflation or whatever else, and the solution if you don't trust the people constructing them is to investigate the particular processes by which any particular one is constructed to see what flaws there are/might be, whether they be minor or totally disqualifying. Otherwise, there is simply no point discussing anything.
Congratulations, this is an observation every undergraduate social scientist and humanities students has about 6 weeks into their studies - in the same sense this is true, history books also have no 'basis to reality' - they are necessarily vast abstractions and simplifications of an infinite amount of possible evidence. Like E.H. Carr says, evidence is like fish in the sea, not in fish on the fishmonger's block, and we are all groping around in the dark in the face of impossibly vast and complex problems of social measurement. However, we don't on that basis dismiss history as a worthless enterprise with no truth value, and nor should it be with statistics. If you find the FBI or the Treasury's statistical work inadequate or too easily manipulable, please don't ever read quantitative history, you might have a heart attack.
I'm not suggesting that all statistics everywhere are bad. I'm saying that these statistics, collected by these people, in present America, are bad, and unreliable, as evidenced by the observation that anyone relying on them just a few weeks ago would have completely different conclusions as to etc. etc.
You want to have some sort of tough guy online moment where you call me a coward for not wasting my time parsing through obvious statistical bullshit of the highest order. Silly goose
Not true at all. The statistics have barely changed and one's conclusions should be exactly the same - a very small decrease moving to a very small increase is not important.
I'm just asking you to explain why it's bullshit, not just refuse to engage with any specifics.
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Rather, most people refuse to because they are cowards about demarcation and it's useful to be tactically hypocritical.
Social sciences are not without any merit but I find the idea that we should submit any authority to something this made up laughable.
When people tell you to do something against your interest and all they can muster as justification is this level of rigor, you just tell them to stop trying to con you.
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Yeah, a lot of government economic stats feel super fake.
For example, the rate of inflation depends on so-called "hedonic adjustments". I'll admit that this is a valid methodology. For example, a TV today is miles better than a TV from 2002. But, as I've written before, these hedonic adjustments DON'T take into account other things like the degradation in service quality. In 2002 the Starbucks bathroom wasn't locked. Today it is. Where's the hedonic adjustment for that?
When we remove hedonic adjustments, inflation is much, much higher than the official numbers. The official numbers also just don't pass the smell test.
Then we get to "unemployment". It's super fake. The male, prime age employment rate was nearly 95% in 1968. Today, it is just 86%. That's 9% of men age 25-54 who are not employed. But they are not counted as "unemployed" either. It's all fugazi.
https://fred.stlouisfed.org/series/LREM25MAUSA156S
Without wishing to be flippant, statistics measure what they measure, and it's absurd to get annoyed because a statistic designed to measure something (i.e. unemployment among people in the workforce) measures that thing, and not something else you think is actually more important. It's not 'fugazi', no-one is trying to pull the wool over your eyes - in fact you prove this very point - if you want you can look at other statistics - LFPR, U-6 Unemployment, whatever you like. Is your objection to the whole concept of U3 unemployment as a statistic. Should we not collect such data because you prefer U6? Seems a bizarre way of interacting with the world.
Do some people not understand the distinctions when a big headline reads 'unemployment at X%'? No doubt, but that is a problem with media literacy not with the statistics.
yes_chad.jpg
Nobody should be reporting on U3. They should be reporting on U6 and LFPR.
It's perfectly reasonable to be annoyed at deliberately misleading statistics.
I mean given that these all measure different things they surely all have there place and importance. LFPR is important, but it is obviously a very distinct social question to 'how many people who want work can't find it', which I would argue is a lot closer to what most people are driving at when they use the term 'unemployment' in common parlance.
'Deliberately'? Again, statistics measure what they measure. If someone misinterprets or misuses a particular statistic, it is not the statistic itself which is flawed but the interpretation. Remember, it is not U-3 is the new innovation but U-6, which only goes back to the nineties. Incidentally, U-6 tracks U-3 pretty reliably over it's total span, so any conclusions one was drawing from U-3 (since change over time is generally the focus) would be pretty much replicated by looking at U-6.
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U-6 includes workers employed part time for economic reasons plus persons 'marginally attached' to the workforce -- those who have looked for a job in the last 12 months but are not currently looking for work.
This is the spread -- U-6 minus U-3, that is, the marginally attached plus the part-time for economic reasons. It tends to follow the unemployment rate, so this is the percentage spread (U-6 minus U-3, over U-6). Neither is particularly high right now.
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No, it isn't absurd. Words have common definitions, which the agency can't just redefine.
If they had called it the "job-seeker-limited jobs index" or something else which can't easily be treated as though it just means the common definition of "unemployed" we wouldn't have this problem. The statistic is, by its name, "designed" to mislead.
When has unemployment not referred primarily those out of work who are seeking jobs?
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"Unemployment" has been limited to that since the 1930s. And the term seems to be mostly limited to the concept that the agency measures.
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It's trivial to change any methodology to get bigger or smaller numbers. The question is if the new number is more meaningful than the old one.
Truflation estimates 26.27% inflation since January 2020. CPI has increased 21.23%. I'll leave it to the reader to decide if CPI is an obviously wrong estimate.
https://truflation.com/marketplace/truflation-us-aggregated
Again, why is U3 fugazi just because you can get a bigger number with another methodology? Especially when you're looking at a subgroup analysis. If we look at prime age LFPR for the entire population, it was 70% in 1968 and now it's 84%.
https://fred.stlouisfed.org/series/LNS11300060
Yes, total labor force participation is up due to women entering the workforce. It now takes 2 incomes to do what a single income did before. This phenomenon led to the one good idea Elizabeth Warren ever had: "The Two Income Trap". This is not a signal of prosperity, far from it.
But men have been leaving the labor force in large numbers. Whereas only 5% of prime age males weren't employed in 1968, today it's nearly 14%. And, of course, this doesn't even reflect the rise of part-time labor.
Because U3 only reflects short-term fluctuations in the labor market, and not the disastrous long term changes which have occurred over the last 50 years. And the media reports on U3 but not on the things that matter more.
For your consideration: the US army doesn't enlist anyone scoring below 10-th percentile on their IQ test. That's 10% of men that the US army considers untrainable, despite having vastly more control over a soldier's life than another employer. Based solely on that, I would expect that there should be at least 10% of men who ought to not be employed.
Where were those men in 1968? Probably institutionalized, and thus not counted in LFPR.
There has been a massive de-institutionalization in the 70s.
Bottom 10% on AFQT is not the same as bottom 10% on IQ test. This would be more like bottom 20% on IQ test. Countries with mean IQs of 85-90 still find uses for these people; otherwise unemployment rates would be much higher. In the US, a high minimum wage and other regulation creates an incentive to choose smarter workers. If you have to pay $15/hour, you're gonna want the smarter worker/.
I am skeptical that IQ tests measure what we think they measure in developing countries. Even those tests that pertain to be context-free and that don't require one to be able to read. It takes intelligence and cunning to hunt and forage, or to run a homestead farm, or to navigate life in a shanty-town. I think that an American with IQ of 70 and a Papua New Guinean with an IQ of 70 differ greatly in how well they can take care of themselves.
The US Army doesn't specify the IQ cutoff; some people estimate it at 83 (that's what I remember from McNamamara's Folly. Standard deviation of IQ is 15, mean 100, so below 83 is 11.5%.
The US Army by law restricts the employment of the next 20 percentiles (11th--31st) to be no higher than 20% of the applicant pool:
The corresponding IQs would be in the 83-93 range.
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5% of the population was not institutionalized in 1968. That doesn’t pass the sniff test.
I appreciate the reality check. I asked Perplexity AI for an estimate of the total number of institutionalized people in US in 1960s, including prisons, mental institutions and institutions for the mentally impaired. The peak for mental institutions was half-a-million a decade earlier (so about 0.3% of the population), the prison population was less than it is now, but as for the mentally disabled:
I know several institutions for the mentally disabled within my area. They range from assisted living to full-on can't-go-outside-without-an-escort (for those who are mobile). They tend to be out of the way, and people who don't have family members (or family members of their close friends) tend not to think about these places.
So I am going to guess that a large portion of the bottom-10%-IQ were indeed in some form of institution that would take them out of consideration of the labor force participation metric.
I would be open to evidence that it was common for mentally challenged men to get hired and work. Maybe with a lower minimum wage, it makes sense. My friend's sister, for example, works at a doggie day-care for like half the federal minimum wage, something like 20 hours a week.
Note that there were more actually literally retarded people at the time due to a higher rate of birth defects.
My guess is that it was pretty common for actually literally retarded people living mostly with relatives to do some unskilled grunt work(eg field hand, heavy laborer- there was still a lot of unmechanized agriculture and ditch digging going on). McNamara’s morons had to come from somewhere, and I’m guessing looking into it might give us a better idea- it seems unlikely the military was recruiting from long term care homes. But we really don’t know.
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@sarker has addressed most of the rest, but just to highlight this point - this is a criticism of the media not of the statistics. FRED or the Treasury or any other statistical body/publisher have very limited control over how the media reports on their statistics, so it's hardly their fault if you think some other measure of unemployment than U3 ought to be more widely reported on.
This is more a criticism of news-as-events than anything specific to statistics. I don't wholly disagree with that broader point - news media does often privilege 'current events' over longer-term analysis - but reporting on unemployment is usually carried out with reference to current transitory conditions, which certainly has a legitimate place and for which U3 is the appropriate tool. In other words, that U3 reporting reflects short term fluctuations is by design.
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The two income trap isn't that women enter the workforce, it's that people live paycheck to paycheck on two incomes rather than one, meaning that there's less slack in the household.
Things are obviously generally not twice as expensive in real terms as they were in the 1960s, though housing is a notable exception. However, the price of housing clearly is being driven by factors other than people having more income.
Okay, but women have been entering the labor force in larger numbers.
By the way, you are equivocating between "employed" and "participating in the labor force". There are not the same concept.
You've yet to show that it's disastrous, unless all you care about is minmaxing prime age male LFPR.
Yes, I think
minmaxing prime age male labor participation rate is a good thing.We don't need more hikikomori and drug addicts who don't work. (I will acknowledge that part of the change is due to people who are studying past the age of 25. But this is also bad).
There is nothing good about the number of men not working going from 5% to 14%.
Conversely, I think we'd be much better off with lower female labor participation. Many women who would prefer to stay home with children feel that they need to work, either for money or for social acceptability reasons.
I somewhat disagree. Getting these people out of the labor force may mean better service for customers and productivity for employers. People who have low inclination to work are probably worse employees.
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I'm not sure how coherent this is. If your objections are primarily socio-cultural - i.e. women would be happier in the home (I disagree but whatever, fine) - then why even bother talking about economics? If your objections are economic then these two goals obviously work at cross-purposes; if the problem is the increasing ratio of the non-economically productive to the productive, women leaving the workforce obviously makes this problem worse.
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You've got a just so story for why women staying home is good and men staying home is bad, but it's easy to make up the alternate story as well. It might go something like:
I don't expect you'll be convinced by my argument, but you should recognize that yours is also only convincing to those already convinced.
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I have the seeds of an effortpost about women’s socially conservative preferences conflicting with the situation on the ground. But so far, it’s just the seeds.
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There are good reasons(although he doesn’t articulate them) to care a lot about prime age male LFPR. Generally fewer to care about female LFPR.
I can imagine a society in which women go to work and keep the place running, and men don’t. My knowledge of men and women points to this being a very bad society.
In contrast if you cut female LFPR to 0, well, strict Islamic countries exist. Saudi Arabia managed to be stable and functional and have a low crime rate. In practice I don’t know of many people that want to go that far- the female LFPR in America 1950 was still well into the double digits, but this was a stable functional society with modern infrastructure.
Gender roles are real. You cannot ask for men to do women’s jobs, or women to do men’s jobs, and expect that they will do them as well as if done by the sex to which they are naturally suited. And at society-wide scales, even small differences add up.
Okay, but we're not trading off 100% male LFPR vs 0%. The question is about 95% vs 86%. The fact that women don't want to work construction or whatever doesn't tell us which one of those two is better.
Fewer, except for respecting the freedom of an individual to choose whether they wish to work or not. Perhaps you can argue that women are driven into the workforce despite not wanting to do so, but you must admit that the opposite was happening in the sixties.
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Not only that, but quite often the older version of the product is no longer available. I may have the option of buying a 4K TV, but it’s not like I could choose an old CRT TV if I wanted one. Or in the case of shrinkflation, if you make packages smaller, than the old version isn’t available. People are not choosing the new one, the old one is gone.
Inflation stats account for smaller packages.
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A CRT TV maybe not, but you could certainly buy an outdated and small normal TV for incredibly cheap.
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Isn’t boring and dull bureaucratic number crunching the opposite of “made up”? I wouldn’t want these numbers influenced by someone putting their finger on the scale because they know better than the data. I’m sure it happens anyways, but I don’t see how your argument justifies what you’re claiming.
"Bureaucratic" doesn't mean consistent and stable, it means arbitrary and unimaginative. You need to calculate how much inflation went up last year. The price of a Honda Civic went up $1000. The price of a Chevy went up $2000. Are those cars in the same categories, or different categories? Do we average them? Then it turns out that although the Civic went up $1000, they added new airbags that promise to save lives. How much is that worth? Let's make up a number. The cost went up by X but the value went up by Y so really that price increase doesn't represent $1,500 of inflation but etc. etc. etc. The economy is endlessly complex, and the measures aren't. So they're very somewhat arbitrary. It's drawing in freehand.
It's not even about, say, a conspiracy to make the numbers look good for someone or some purpose. (Although that happens: Boss wants evidence that raising interest rates is good so let's give it to him. I remember this famously happening in how CBO came up with estimates for Obamacare's impact on the federal deficit.) But it's not really a conspiracy. It's garbage in garbage out. You would expect this to fall apart for complicated situations, such as what we have right now: the economy is great for some Americans and terrible for others.
You don't need to posit invented scenarios like this, you can just go and check what they change.
For instance, in the UK they publish the change in CPI weights every year, which can be found here https://www.ons.gov.uk/economy/inflationandpriceindices/datasets/consumerpriceinflationupdatingweightsannexatablesw1tow3.
The changes are seriously miniscule, with one or two exceptions due to the recovery of things like pubs and restaurants post-covid, but even these changes are not large.
What's particularly notable is that the Eurozone, US and UK all had extremely similar patterns of inflation since 2010, even in periods where it wasn't a notable partisan issue. Presumably all the administrators in each of these countries or blocs cannot have had identical internal or political incentives across time, nor does it seem likely they mere happen to have made identical mistakes in the exact same sequence. The only answer is that they are broadly measuring something real across the global economy in at least a relatively accurate way. No doubt disputes and decisions over changing weights can impact figures at the margin, but the overall pattern is generally going to be reliable.
This is the entire purpose of national economic statistics - to provide an overall average for the entire economy.
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The figure that I'm most interested in would be something like, "What is the least amount of money someone needs to live a respectable life this year?" Something like the poverty line, except instead of just looking at basic needs it looks at how much someone would need to spend to live according to the regulations and societal expectations we are subject to. This number would be widely different between someone who bought their house before 2010 vs someone who is renting or bought a house more recently. There are a few categories that could be evaluated separately, like lifestyle to be a respectable DINK in a big city, lifestyle to be a family of four in a suburb, etc. The interesting thing would be to pick a lifestyle and stick with it for a decade, seeing what the minimum amount a family could spend to stay in that lifestyle over time. That is what people think inflation is tracking, or wish inflation was tracking, based on how people reference inflation in arguments.
Someone has been tracking "Average Household Expenditures" which seems like an ok, if imperfect proxy for what I have in mind. https://www.bls.gov/cex/tables/top-line-means.htm.
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No, it means that they are "made up" out of the distortions and idiosyncracies of a horribly-kludged procedure on its 44th revision from an original 1987 typewritten spiral-bound handbook, which has been subject to a constant distortionary tug-of-war to drag it closer to the political expediency of the day, or the latest appointee's personal policy judgment.
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