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Right wing news media today reporting a "quiet" revision to FBI crime statistics, revealing that violent crime rose in 2022, contrary to their initial September 2023 report (and broadly contrary to a recent historical trend).
As the linked article notes, adjustments of this nature are not uncommon, but this particular adjustment flies in the face of fact checks and hit pieces directed against right wing media and political candidates who, apparently, knew better than the FBI. I have been unable to find any retractions thus far, however (and of course do not expect any).
The FBI's process for assembling crime statistics has been under revision for a couple of years, leading to a variety of difficulties for those (like reporters) accustomed to relying on the statistics to establish the truth of perceived trends. As far as I can tell, the initial revisions were motivated by the same sort of social engineering goals that led realty websites to remove crime maps from home search tools. But now maybe some of those changes have been rolled back? It's not totally clear to me what's happening there, beyond a government bureaucracy seemingly looking for ways to prevent the unvarnished truth from generating too much wrongthink while also staving off accusations of being even more useless than usual.
(Or maybe there's a "Schrodinger's Violence" problem, where they need to show increased violence to make strong arguments against the Second Amendment, while also showing decreased violence to bolster
Biden'sHarris' claim tore-electability?)While violent crime is still much lower, per capita, than it was ~35 years ago, it is of course still much higher than it was circa 1960, when the United States was a very different place, demographically. The 21st century nadir seems to be around 2012, and the trend since has been a slight but relatively persistent rise.
Will the FBI's adjustment make a difference in the race for the White House? I guess I'm skeptical; left wing news outlets don't appear to be reporting on the adjustment at all, and since it's about 2022, it's "old news" anyway. The falsehood is out there, its work is done; the truth has only just managed to lace its shoes, and here the race is almost over.
Crime is a fascinating but confusing topic because there are so many confounders and ways to interpret the data. What is not counted is as important as what is counted. Homicide rates are affected by treatment; if someone survives due to emergency room treatment and medical advances, it's not a homicide, so it becomes either an attempted homicide or downgraded to something less serious. Crime rate are also affected by reporting and demographics. On twitter, the narrative I see now is that crime of all types is much worse, but this is masked by underreporting or crimes not being prosecuted.
the question is also why are stats revised? how does the the methodology work? If the FBI uncovers a body in 2024 but it's determined the homicide occurred in 2023, then presumably it would be added to the 2023 stats.
In regard to demographics , if today's population were transported to the 60s, would crime be worse? I think it would be. Worse demographics and people being angrier in general and lower social trust could account for crime being worse, not that police are less competent today.
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I followed the links to the original reporter, and then did a not-too-deep dive into the FBI Uniform Crime Statistics.
The way I see it, there are three separate questions at hand:
1 How many violent crimes were there in US, in reality? (In time-series sense.)
2 How does FBI collect and measure (or estimate) that statistic? Have they changed that methodology? How do their estimates compare to other good estimates (like the National Crime Victimization Survey, for example)?
3 How have politicians used the FBI statistics.
The first question can only be glanced through a dark distorted glass of statistics, and it's always important to remember that any specific estimates have a particular methodology, which can be more or less flawed.
For the second question, the important bit of info is that the FBI changed its methodology at the beginning of 2021 (PDF warning, my highlights):
Looking at the graph of all violent offenses in US in the past 5 years, it's clear that there are statistical artifacts. For example, before 2021 every year has a bump in December, which is unlikely to correspond to actual huge increase in crime and more likely is police precincts catching up on their paperwork.
The other important bit of info is that, in that transition year 2021, only 2/3rd of population are covered by the reporting precincts, as opposed to before (95%-ish) or after (90%-ish). So any comparison to the year 2021 will be junk.
I can't find the links right now, but my recollection is that the stats for the year 2022 were adjusted because a lot more precincts caught up on their reporting for that year.
PS. Yes, the FBI should be more responsible in clearly communicating their updates to the public.
IIRC, the FBI classifies most Hispanics as white, so there is no way to see the crime rate of (what most people think of as) white perpetrators except in the rapidly dwindling number of areas where Latinos are rare or absent.
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Without fail, huge chances of any major statistical trend being an artifact of changing methodology. Thanks for doing the work.
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It would be crazy if this is just some long term strategy by the FBI to make themselves look good. Each year they publish juiced figures that the media can gush over then later on they retract the juiced figures. Retracting the juiced figures is necessary because otherwise each year the FBI would have to make progressively larger adjustments until juicing was no longer feasible.
Also, why does the FBI need to make itself look good to the public? It's not an elected position. Also, it can work either way: rising crime is evidence more funding is needed; falling crime is evidence funding is working.
The explanation is that the FBI doesn't need to make itself look good to the public, it needs to make the party in power look good to the public, so they can get the funding they want.
they have investigated many prominent democrats this year or recently. For example, Oakland Mayor Sheng Thao, and also Eric Adams, Robert Menendez.
Cause or effect? Are these Democrats investigated because they have been declared PNG by the party? Or are they out with the party because they were investigated (or for the underlying reasons)?
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Aren't organizations incentivized to make the problem they're fighting look worse? Students reach new lows on standardized tests - give more funding to schools; We're falling behind in a particular field of research - give more money for researchers; No one wants to pay for elite art - subsidize elite art; More generally, if you express even the faintest interest in supporting a charity or a nonprofit, they will bombard you with newsletters about how terrible things are, and how the world will end if you don't send them money RIGHT NOW!
And correct me if I'm wrong, but aren't most of these crimes outside of FBI jurisdiction to begin with?
Whoever those fudged numbers are supposed to make look better, it's probably not the FBI, and outside political pressure seems pretty likely, especially when we know for a fact it goes unpunished in the event it's discovered, and undisputable.
Organizations are incentivized to make the problem they're fighting look maximally affecting - you don't want to push your constituents over the edge to thinking that the problem is insurmountable. You're also incented to make your own efforts look seriously busy and important - or at least like you're forestalling worse outcomes - otherwise you get a reputation as useless.
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It's weird how that happens. We should do the opposite. When a problem gets worse, we should defund and replace the failing organizations who are responsible. (For example, San Francisco area homeless orgs).
On the other hand, when organizations succeed we should give them more money (For example, SpaceX).
I'd argue we should do neither. whether the amount of homeless went up or down, it may not have anything to do with the homeless orgs at all. They didn't make the problem, and they may deserve anywhere from 0-100% of the credit for each person who is no longer homeless, or 0-100% of the blame for failing to solve the problem.
For best results, you'd need metrics that represent what the homeless org did, how well it worked, and the reasons why it didn't work better. In the FBI's case it would be things like the number of cases investigated vs solved, time spent per investigation, etc.
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Lott's website has a better breakdown of how the revision applied, and it's worth noting that there were both decreases in the (non-rape?) 2021 numbers and increases in the 2022 ones.
More subtly, it shows that only through such third-party groups can such revisions be visible at all.
There's a deeper problem where everyone apparently knew this metric was bullshit, so there's no reason to think the newer numbers are 'real', but it's a little aggravating to see these things getting used as both political and policy sledgehammers on one hand and dismissed wholesale in the other.
Apropos of nothing, still no response from the ProPublica author from a few weeks back. Her coworker was so certain "Reporters love talking to people about journalism", too.
Lott's blog post raises more questions than it answers. At first, I had no idea where he gets his headline claim that 1,699 murders were 'missed'. In the spreadsheets he himself links, the old data shows 21,156 murders for 2022, and the new data 21,781, and the rates are in fact identical (at least to one decimal place). The difference in total violent crime doesn't match his headline estimate either.
On further inspection, what he's actually done is added together the revisions from 2021 and 2022 irrespective of whether the revisions were up or down. So since the FBI has revised their 2021 homicide estimates down slightly and their 2022 estimates up slightly he's added the changes up and got his figure of 1,699. However, this means his headline that the FBI 'missed 1,699 homicides in 2022' is flatly wrong in any reasonable reading. It's such a bizarre way of reaching that number it's hard not to chalk it up to a deliberate attempt to make the change seem bigger than it actually was.
If we take the blinkers off for a second, these revisions are really not that meaningful. All that's happened is that a very small decrease has been turned into a very small increase - politically this may be important insofar as appearing to be moving in the right direction is useful, but in reality it's just meaningless noise. The old figure of 377.1 total violent crimes per 100,000 has been replaced with a figure of 377.6, a 0.13% difference. Utter nothingburger that should not change anyone's opinion on anything.
The revisions themselves are not surprising and probably shouldn't be taken as strong evidence of anything in particular. But it's not a "nothingburger" when the news media, politicians, etc. use a negative number to "fact check" and excoriate opponents, only to later have that number instead turn out to be positive. Either the numbers mean something or they don't. If the numbers do mean something, then all the news corporations that said "Trump was wrong" should now be printing stories saying "sorry, turns out Trump was right." If the numbers don't mean anything, then all the original news stories saying "Trump was wrong" should have been laughed out of the room from the get-go. Publishing numbers widely because they make your opponent look maximally bad, then ignoring revisions to those numbers, just shows how much the corporate news media (sometimes, Fox News excepted) has straightforwardly become the propaganda arm of the Democratic Party.
That is far from a "nothingburger."
Not necessarily. In fact, because the revisions are so small the numbers mean pretty much exactly what they did before - and if they do mean anything, what they probably mean is 'crime didn't deviate from it's previous trajectory a great deal'. Of course, this in turn doesn't necessarily imply any conclusion about the impact of BLM/changes to police policy, as who knows, without it maybe crime would have gone down by some non-trivial amount. News media should report on the change, but it shouldn't change their conclusions about anything. Trump saying 'crime is only going up' is still not really a statement that can be justified by the statistics in all but the most trivial sense. Even post-revision the 2022 figures were similar to the 2021 and 2020 figures (notwithstanding the problems with the 2021 statistics). While some of the fact-checking on Republican rhetoric on crime was probably over-zealous, that rhetoric was still wildly misleading. There was no surge in crime as constantly espoused by Republicans. Anyone saying 'crime is plummeting' was lying too (even if the pre-revision stats had turned out to be correct), but I don't think anyone was following that line as prominently or as vociferously as Republicans pursued the reverse narrative.
Stuff like this;
is still utterly false. In fact 2023 crime is back down below 2019 levels. This proves nothing either because the margins are so fine. But Trump is and was wrong.
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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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Makes me wonder how many more “adjustments” will be made to 2023 numbers in the future. Although I’m not surprised at all yet another government agency is fudging data in a partisan manner.
And didn’t one of the debate moderators “fact check” Trump using this very same FBI data? The election has already been rigged, yet again, by misinformation peddlers.
The data, though adjusted, still does not bear out Trump's ludicrous rhetoric. All that's happened is that a very small decrease has been turned into a very small increase, before we even consider the question of the transitionary statistics of 2021. Even under these new figures rhetoric like the below is totally wrong.
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I haven’t read up on this case as much, but if it’s anything like the employment stats revision, are you implying that official stats shouldn’t be revised in the face of new information?
I dispute the information was new to the FBI
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