Summary: The US economy is one of the strongest of the developed nations, which has allowed us to avoid reforming our decayed political system, our mad unprofitable empire, and our insane military spending. The cheering over today’s employment report — showing continued slow growth — reflects this optimism. It’s delusional, looking at the effect while ignoring the cause. A broader perspective shows a darker picture.
US Public debt:
- $5.1 trillion at the recession’s start in December 2007
- $10.1 trillion one year ago
- Now $11.3 trillion
So the Federal government has borrowed $6.2 trillion since the downturn began. It’s borrowed $1.2 trillion during the past year, over 8% of GDP. For comparison, a 3% deficit is high — and a 4% deficit is critical.
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Let’s ignore the secondary question about the wisdom of this spending, and consider its effect. The combination of fiscal and monetary policy — both dialed into the red For Emergency Only zone — has produced 4.5 years of slow growth following the crash.
That shows the true state of the US economy: weak. Years in the intensive care ward have produced only a semblance of good health. The vital signs are strong, but only due to the drugs and mechanical assistance. That’s why the Fed began QE3 instead of starting to unplug the patient.
To boost morale our leaders lie to us about this simple truth. Being foolish and passive, we eagerly believe what we’re told. While this maintains spending and investment, this false security saps any willingness to make the deeper reforms we desperately need. This is the unanticipated side-effect of the otherwise-successful (in a narrow sense) economic treatment.
Update: for more about the latest jobs report
- Robert Waldmann (Department of Economics, Tor Vergata University of Rome))notes the Employment/Population ratio is back to the level of Reagan’s “morning in America” level
- “Constant-demography Employment (Wonkish But Relevant)“, Paul Krugman, New York Times, 6 October 2012
- A look at the coming Holiday hiring season by David Rosenberg, Chief Economist of Gluskin Sheff via Zero Hedge
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My generation, the Baby Boomers, whose biggest buzz words was; “don’t trust anyone over thirty” back in the 60s have been and still are for the most part, running this country.
Yes, the Latch-Key generation is partly in the mix and the young men and women under thirty-five have got to be saying,” don’t trust these older folks to even take out the family pet”. They can’t be trusted and to the young generation I would say, you are absolutely right, they cannot be trusted. I know I grew up with them.
The Baby Boomers, and remember, I’m one of them, are Democrats, Republicans, over religious, over atheists and just down right selfish. They want it now! My generation burnt down campuses, ran our Vietnam veterans into the ground with their trash talk, piled up the drugs and booze and then passed it on to the younger generation.
Even Obama agrees with the Republicans that Big Bird needs to go. Obama doesn’t mind stabbing Big Bird in the back while him and the rest of congress sends billions of dollars to countries that stab us in the back. Can you really trust this guy? Let’s cut education to the bone so we can spend billions of dollars for campaign funds, off-shore accounts, oil and mineral rights in Iraq and Afghanistan and let our soldiers do the dirty work with their lives. Their lives mean more to me than the President, Congress, the Supreme Court and their special interest and of course, the Fortune-500 Hundred companies that get subsidized to have off-shore accounts in the Bahamas.
Of course, I should get out of my sorry attitude and be warm and fuzzy over the new job report that unemployment dropped 3 tenth of a percent. Try putting that in your gas tank and see how far you get. Truth is, can you trust those numbers? I can’t but of course, I’m just an old cranking Baby Boomer.
Ai caramba! The caption on that second pic..
And you called my comment a few posts ago “brutal”?
In this context, yours and mine about America, “brutal” is both accurate and operationally useful.
I imagine a guy in a ‘vette, taking it off a cliff at 100MPH. The tires are shrieking, he’s wearing a glitter-covered bow-tie, and everyone says “THAT’S INSANE” but, yeah, it’s GOT a V-8 ENGINE!
I think that what’s going on is that, basically, we’re doing a nation-level rendition of “My Way” and taking (or bombing) everone along with us.
Very brief and amateur summary of changes in BLS seasonally adjusted employment changes from August to September because the MSM seems only to be interested in the political ramifications, not the numbers themselves.
Size of employment force: +418,000 (always good to have more potential workers)
Participation percentage: +0.1% (first time this number has risen in several months, another positive)
Employed: +873,000 (a shocker, first time this number has gone up in several months and it went up by a lot)
Unemployed: -456,000 (a big pleasant surprise, down more than expected but not completely out of line with past numbers)
People losing their job: -468,000 (another pleasant surprise, much better than in past months)
Part time workers: +322,000 (a reversal of the trend of recent months)
Full time workers: +551,000 (in line with expectations based on numbers above)
How real are these seasonally adjusted numbers? I can’t say. I checked out the non-seasonally adjusted employment numbers but they are much less comprehensive than the seasonally adjusted numbers and all I could do was confirm overall trends such as population growth. I keep wondering if the BLS started using new data gathering or refinement techniques because of the sudden large shift in numbers.
My wife has been gathering local retail employment information for her own reasons and the local trend line is not nearly as positive. The big chain stores are maintaining the same or very slightly higher employee counts. The smaller stores are mostly in tough shape and are not planning on hiring many seasonal workers for Christmas, just giving more hours to their current employees.
Final note: I just contributed to the FM tip jar and encourage others to do likewise. We all get a lot of benefit from FM’s hard work and he deserves some financial encouragement to keep going with fewer commercials.
Anyone who cares to take the time can crunch the numbers for themselves. You might be surprised. The number to watch is the “Labor Force” numbers.
Data From Google Public Data [http://www.google.com/publicdata/directory]
Sources: US Census Bureau, Bureau of Labor Statistics
Data NOT Seasonally Adjusted.
2007, July
Population: 301.231 million
Labor Force: 154.871 million
Employment: 147.315 million
Participation Rate: 51.4% (labor force/employment (from data))
Unemployment Rate: 4.9% ((employment-labor force)/labor force (from data))
Unemployment Rate: 4.9% (BLS published)
2012, July
Population: 311.592 million
Labor Force: 156.526 million
Employment: 143.126 million
Participation Rate: 50.2% (labor force/employment (from data))
Unemployment Rate: 8.6% ((employment-labor force)/labor force (from data))
Unemployment Rate: 8.6% (BLS published)
2012, Sept.
Population: 312.153 million (Est.)
Labor Force: 155.075 million
Employment: 143.333 million
Participation Rate: 49.7% (labor force/employment (from data))
Unemployment Rate: 7.6% ((employment-labor force)/labor force (from data))
Unemployment Rate: 7.6% (BLS published)
Deltas 7/2007 to 9/2012
Population: +3.6%
Labor Force: +0.1% <<<<<THIS MAKES NO SENSE!!!!
These numbers aren't just cooked, there burnt!!!There has been no societal shift or increase in welfare that would have accounted for a decrease in participation rate from 51.4% to 49.7%. That represents 5.3 million people, who, per the BLS, have just decided they no longer needed to work. Huh?
CALCULATE LABOR FORCE RISE TO MATCH POPULATION RISE:
2012, Sept.
Population: 312.153 million (estimated)
Labor Force: 160.486 million (calculated)
Employment: 143.333 million (BLS)
Participation Rate: 51,4% (labor force (calculated)/employment (BLS))
Unemployment Rate: 10.7% ((employment-labor force(BLS))/labor force (calculated))
Unemployment Rate: 7.6% (BLS published)
The rate of 10.7% doesn't even count the underemployed and people working 'part time not by choice' and others. The inclusive rate, U-1 through U-6, is probably closer to to 25%
When we get to these types of examinations, the 'experts' will come out to say one of two things; The first is 'it's complicated'; the second is 'you're not qualified' to comment. Two ways of saying the same thing: "The numbers are what we say they are, so shut-up!" Klein (1) can go screw himself… these numbers are not good!
1. (http://www.washingtonpost.com/blogs/ezra-klein/wp/2012/10/05/september-jobs-report-debunking-the-jobs-report-conspiracy-theories/)
Correction: Participation calculation should read “population/labor force (from data)”, not “labor force/employment (from data)”, 3 places, and “population(estimated)/labor force (calculated)”, not “labor force (calculated)/employment (BLS)”, 1 place.
“These numbers aren’t just cooked, there burnt!!!”
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“When we get to these types of examinations, the ‘experts’ will come out to say one of two things; The first is ‘it’s complicated’; the second is ‘you’re not qualified’ to comment.”
There’s not much to add to Frank’s comment, as he’s given sufficient explanation to his flawed analysis. However, for those who like detail I’ll add a little more color.
As anyone familiar with demographic and economic data knows, this is not like counting apples. In this case, comparisons over time tell us nothing without knowing the demographics. Especially the number of people in the volatile 16-25 and 55+ groups, and the changes by gender (women’s participation often more volatile). Younger people flow into and out of the military (depending on its expansion & contraction), and respond to slow economy by extending their education. Older people respond to economic people by retiring (voluntarily or otherwise) and obtaining certification as disabled.
So Frank’s wild claims require a more sophisticated analysis than his sidewalk chalk drawings.
(2) “
increasedecrease in participation rate from 51.4% to 49.7%. That represents 5.3 million people, who, per the BLS, have just decided they no longer needed to work. Huh?”Frank’s “huh” nicely sums up his “analysis”. A change of 1.7% over five years is not large. Especially given the aging of the boomers (ie, which will inevitably lower labor force participation over time, until they start to die in large numbers).
(3) For anyone who would like to understand these numbers, the BLS database provides easy facility to generate tables and graphs of the important numbers and ratios over time.
(4) Historical comparisons have to adjust for changes to the data. For the Current Population Survey, that includes the Population Control Adjustments. The results from Census 2010 were incorporated into CPS population controls with the release of data for January 2012. CPS estimates for January 2000 through December 2011 reflect population controls based on Census 2000. See their notes here.
Conclusion
It’s not like counting apples, and one does need to understand the data. Which is why we have experts. Ignorance and self-confidence are not sufficient.
“How real are these seasonally adjusted numbers? I can’t say. ”
I think Pluto means “how reliable”. Only so-so. The seasonal swings in many economic data series are larger than the small “real” changes. Worse most of these tend to be volatile (ie, random, noisy). Without seasonal adjustments the high-frequency data is useless except on a year-over-year basis.
But there is no highly reliable way to do seasonal adjustments. So they do the best they can. Which is why this data has to be regarded with care, and evaluated only as a broad picture assembled from many sources. Some are reliable and high-frequency, but narrow (eg, auto and chain-store sales, new claims for unemployment). Some are broad, but highly conceptual and heavily revised (eg, GDP and GDI).
This is why we have experts — economists — to analyze this. It’s difficult to do well, esp since the data collection agencies are grossly underfunded, so their forecasts are only moderately reliable (eg, they tend to miss inflection points).
“My wife has been gathering local retail employment information for her own reasons”
The best source of this: the ICSC-Goldman Sachs Weekly Chain Store Sales Index. The news services report it. The actual report is subscription-only: here’s their September 18 report.
“the local trend line is not nearly as positive.”
Local data tells us nothing about the overall US picture. It’s a large nation.
Follow-up about retail sales:
(1) The best open source for the ISCS data: retailsails.com — it’s down not, being rebuilt. But they report the weekly number, and provide a graph and table of past data.
(2) There is also the Johnson Redbook Service, but I am not familiar with it.
(3) The monthly auto sales are not only useful indicator of overall consumer activity, but also economically important by themselves.
I really don’t care for point by point responses, but there is a lot to cover.
FM: “There’s not much to add to Frank’s comment, as he’s given sufficient explanation to his flawed analysis. However… ” “Frank’s wild claims require a more sophisticated analysis than his sidewalk chalk drawings.”
An Ad Hominem attack along with an Appeal to Spite. Nice.
FM “(2) “increase in participation rate from 51.4% to 49.7%. That represents 5.3 million people, who, per the BLS, have just decided they no longer needed to work. Huh?”
Frank’s “huh” nicely sums up his “analysis”. A change of 1.7% over five years is not large…”
First, that is a mis-quote. My post was “a decrease in participation rate from 51.4% to 49.7%”. A decrease in participation rates over a period that saw the population increase by 11 million! That makes absolutely no sense.
FM: “(3) For anyone who would like to understand these numbers, the BLS database provides easy facility…”
No. No they do not. BLS data that is readily available tends to overly granular, with all sorts of adjustments and interpretations applied. The readily available data is broken down by age, by sex, by race, etc, is “Adjusted”, and availabe in reports with conclusion and interpretations attached. Whole, raw data, the only kind that really matters, is hard to come by on the BLS. Which is why data mining sites like Google Public Data are a godsend. Since the calculated unemployment rates from Google Public Data match the BLS published rates, I have some confidence in the data from Google… And my interpretations.
FM: “(4) Historical comparisons have to adjust for changes to the data…”
That is not correct. If your comparing July ’07 to January ’07, there may be some validity to adjusting data. Basically, it smooths out the cyclical data spikes and makes for nicer graphs, but little else. If your comparing July ’07 to July ’12, seasonal variability is simply not applicable.
FM “It’s not like counting apples, and one does need to understand the data. Which is why we have experts. Ignorance and self-confidence are not sufficient.”
Sorry, if one sets aside the spin, the adjustments and “understanding” of experts, it is a whole lot like counting apples. And more Appeals to Ridicule! Nice.
Just for sh*ts and giggles, here is a more granular look at the increase in workforce for the years 2007 and 2012 (estimated). Changes in military participation, incarceration, graduation and higher education rates, even if great relative to themselves, will have little to no impact on the numbers below. Further, they are not broken out in the examples below.
2007
Population: 301.1 million
Death Rate: 8.26/1000
Birth Rate: 14.16/1000
Growth Rate: 5.9/1000 or 1.8 million more people in 2007
Children born 1991: 4.1 million
Estimated Childhood Deaths 1991 to 2007: 450,000
16 year olds eligible for work force: 3.7 million.
Deaths: 2.5 million
Net Workforce Gain: 1.2 million
2012
Population: 313.8 million
Death Rate: 8.39/1000
Birth Rate: 13.68/1000
Growth Rate: 5.3/1000 or 1.7 million more people in 2012
Children Born 1999: 4.0 million
Estimated Deaths 1996 to 2012: 448,000
16 year olds eligible for work force: 3.6 million
Deaths: 2.6 million
Net Workforce Gain: 1.0 million
Average the data over the six years and you get an estimated gain of over 6-1/2 million eligible workers. When you account for the Military enlistments, incarcerations and school enrolments, the 5.6 million 2007-2012 labor force increase calculated in my previous post comes much closer to the data then the ridiculous 0.2 million 2007-2012 labor force increase published by the BLS. I really don’t need an expert to explain that to me. I really don’t.
* Population, Birth and Death Rate (number/1000 population) from “http://www.indexmundi.com/g/g.aspx?c=us&v=26”. Different sources use different basis dates and will not mach (error observed +/- 0.5%).
* Birth data from 1991 and 1996 from “http://www.infoplease.com/ipa/A0005067.html” used for people entering work force 2007 and 2012, respectively.
* US deaths per year for all children 0 to 19 years of age approximately 28,000 from “http://www.cdc.gov/safechild/images/CDC-childhoodinjury.pdf”.
* Retirement numbers difficult to come by, so will use death numbers (ultimate retirement). Note: Baby boomer retirement begins in force after 2013, so retirement numbers may accelerate, assuming they will have the where with all to retire.
Frank’s reply is typical of these. He has no idea what he’s talking about, and makes stuff up. Based on hundreds of such previous discussions, this is a waste of time. But I’ll make a few point.
(1) “An Ad Hominem attack along ”
Ad hominem is Latin for “to the man”, referring to an attack on the person. Saying his analysis is “flawed” and little more than “sidewalk chalk drawings” is not. Nor is saying that “his claims are wild”.
(2) “an Appeal to Spite.”
That’s an reply that exploits existing feelings of bitterness. There is no such thing here.
(3) Misquote “increase” sb “decrease”
Thanks for catching that typo. Correction made.
(4) “A decrease in participation rates over a period that saw the population increase by 11 million! That makes absolutely no sense.”
A nice demonstration that Frank has no idea what he’s talking about. Participation rate can change either way as the population changes. To mention one obvious scenario: boomers retire while birthrate increases = increased population while participation rate drops.
(5) “BLS data that is readily available tends to overly granular”
Absurd. “Overly granular” implies that they only show detail, not the high level aggregates — which is false.
(6) ““Adjusted”, and availabe in reports with conclusion and interpretations attached.”
It’s also available in simple tables, with no conclusions or interpretations.
(7) “Whole, raw data, the only kind that really matters, is hard to come by on the BLS. Which is why data mining sites like Google Public Data”
There is almost no such thing as “whole raw data”. Frank just doesn’t know what numbers he’s using (Google just collects it from primary or secondary sources). For example, even data from the decennial Census is heavily processed (this controversy has burned for a long time). The annual Census numbers are from models based on limited data.
(8) “I have some confidence in the data from Google”
Another tell, no idea what he’s working with — confusing the transmission vehicle (Goggle) for the source (Census, and probably BLS too).
(9) “That is not correct. If your comparing July ’07 to January ’07, there may be some validity to adjusting data. Basically, it smooths out the cyclical data spikes and makes for nicer graphs, but little else. If your comparing July ’07 to July ’12, seasonal variability is simply not applicable.”
Totally wrong; more aggressive ignorance. Read the memo about changes to population controls, and the effect on historical comparisons of updating data from once census to the next.
(10) “it is a whole lot like counting apples.”
There’s no point in continuing after such a comment. Life is too short. Frank won’t learn, and I doubt any other readers care about such nonsense.
Whoa!
“…makes stuff up…”, “…exploits existing feelings of bitterness..”, “…no idea what he’s talking about…”, “…Absurd…”, “…Another tell, no idea what he’s working with…”, “…Totally wrong; more aggressive ignorance…”
Yes, “To the man!” And quite aggressively. Why?
FM: “Frank just doesn’t know what numbers he’s using (Google just collects it from primary or secondary sources).”
As noted… Google data is from BLS and Census Bureau. Yes, one can find the data on those sites, but it’s is not “easy”. So I knew exactly where I got the numbers from. The reason for the “test” of checking the calculated unemployment rate vs. the calculated rate was to verify and show data integrity. Coming at a problem from two different directions and getting the same answer lends confidence in that answer. It’s a very old method.
FM: “Read the memo about changes to population controls, and the effect on historical comparisons of updating data from once census to the next.”
We talking about 2007 data vs 2012 data, along with birth figures from the ’90. It’s a short period of time. There have been no pogroms, plagues, mass immigration, mass emigration. invasion or even meteor strikes that would have created anomalous discrepancies in data requiring ‘adjustments’. They obviously make adjustments, and quite frequently. Why?
FM: “There’s no point in continuing after such a comment. Life is too short. Frank won’t learn, and I doubt any other readers care about such nonsense.”
Why the hostility? I can understand you don’t agree with me. You may even think me simple. But if I’m wrong, and I’m that simple… Just saying I’m wrong and you’re right don’t make it so. And making it personal is quite petty. How ’bout taking a step back.
Frank raises an important point, which deserves attention. But before that two small points.
First, a small but illustrative point: “We talking about 2007 data vs 2012 data, along with birth figures from the ’90. It’s a short period of time.”
Frank will write hundreds of words about something he knows nothing about, but will not read a short memo that explains this point.
This is just one of the many relevant factors Frank ignores, which together make the difference between reliable analysis and Franks’ “These numbers aren’t just cooked, there burnt”. That’s why we have experts who understand these things, and don’t rely on cocksure do-it-yourselfers.
Second, a small but important point: “But if I’m wrong, and I’m that simple … Just saying I’m wrong and you’re right don’t make it so.”
You might read my long comments giving specific rebuttal points. They are not “just saying {Frank’s} wrong”.
Now the important point: “Why the hostility?”
This is a vital point about America today! People like Frank believe they can insult government experts, even impugn their integrity, with little understanding of the subject, with less in the way of evidence. Note Frank is not raising questions (always a good thing), but making serious charges. Why not? They’re not here to defend themselves. But should somebody stand to defend these hard-working and (in my experience) honest people, Frank’s feelings are bruised.
That Americans today feel so free to lightly insult experts says much about our degenerate character. For example see the non-expert current discussions about economists and climate scientists. Both sides in the debate frequently have fun mocking the work, skills, and character of experts on the “other side”. Perhaps this aggressive ignorance is one reason we’re unable to clearly see the world (described as a broken OODA loop in the many posts on the subject at the FM website).
Information is important, and words have consequences. We might face hard times ahead, and this fecklessness might prove a weakness we can ill afford.
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Wait how can the household survey differ so much from the establishment survey ? Well let’s see it could be a funny sample. Probably part of it but not all of it. It could be that the birth/death of firms model of the establishment survey has missed a turning point (as it does).
I think we can guess two things. First that the CPS happens to have oversampled the employed. Second there are an unusually large number of brand new firms (must be entrepreneurs who can’t read polls counting on #Romneystrength).
My claim is simple corrections to the establishment employment growth estimates are correlated with (household survey – establishement survey). Establishment better than household doesn’t mean that the lambda in best the estimate of the form
(1-lambda)establishment + lambda(household) = 0
We need data on revisions.
From the Robert’s Stochastic Thoughts, website of Robert Waldmann (Department of Economics, Tor Vergata University of Rome):
The employment to prime age (25-54) population ratio has increased since the trough (unlike the employment to working age (16-5) population ratio. … the employment to prime age population ratio is now just about identical to what it was when Ronald Reagan was overwhelmingly re-elected in large part because it was morning in America (the unemployment rate was 0.5% lower then).
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[caption id="attachment_43804" align="alignnone" width="367"]
From Robert Waldmann, from Calculated Risk[/caption]
“Constant-demography Employment (Wonkish But Relevant)“, Paul Krugman, New York Times, 6 October 2012:
These days everyone knows that the unemployment rate is a problematic measure, because it can fall not because more people are working but simply because fewer people are looking for work. (This isn’t what happened in September, but it has been an issue in the recent past). An alternative is therefore to count employment rather than unemployment; one simple measure is the employment-population ratio, which suggests no improvement for years …
But this measure too has problems; it’s the fraction of people 16 and over at work, which means that the denominator includes a rapidly growing number of seniors, who presumably don’t want to keep working. How can we correct for this demographic bias?
One answer, which I’ve used before, is to focus on prime-age adults, between 25 and 54; Calculated Risk did this yesterday, and pointed out that there has been some real improvement over the past year. This is a good quick-and-dirty approach. But it can lead to (false) accusations of cherry-picking, and it also throws out information.
So here’s an arguably better measure: constant-demography employment, which shows what would have happened to the employment-population ratio if the age structure of the population had stayed constant.
For my calculation, I’ve divided the population into three age groups, 16-24, 25-54, and 55 plus, for which employment-population ratios are available in the BLS databases. (Scroll down and use the one-screen data search). I’ve then taken a weighted average of these ratios, where the weights are the 2007 shares of each group in the civilian noninstitutional population. And here’s what you get
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[caption id="attachment_43808" align="alignnone" width="480"]
Krugman, NYT, 6 October 2012[/caption]
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But this measure too has problems; it’s the fraction of people 16 and over at work, which means that the denominator includes a rapidly growing number of seniors, who presumably don’t want to keep working. How can we correct for this demographic bias?
One answer, which I’ve used before, is to focus on prime-age adults, between 25 and 54; Calculated Risk did this yesterday, and pointed out that there has been some real improvement over the past year. This is a good quick-and-dirty approach. But it can lead to (false) accusations of cherry-picking, and it also throws out information.
So here’s an arguably better measure: constant-demography employment, which shows what would have happened to the employment-population ratio if the age structure of the population had stayed constant.
For my calculation, I’ve divided the population into three age groups, 16-24, 25-54, and 55 plus, for which employment-population ratios are available in the BLS databases. (Scroll down and use the one-screen data search). I’ve then taken a weighted average of these ratios, where the weights are the 2007 shares of each group in the civilian noninstitutional population. And here’s what you get:
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[caption id="attachment_43807" align="alignnone" width="367"]
Krugman, NYT, 6 Oct 2012[/caption]
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Aha. So there is real if modest improvement over the past year. Also, the September numbers looks not like an aberration but like a return to trend from what looks like noise in the data over the previous couple of months.
This story is, by the way, broadly consistent with the payroll data, from a different survey, which also suggest employment growing somewhat faster than population.
So contra Romney, this is a real recovery. Modest, but real. Unless, of course, you believe that there’s a conspiracy of socialist statisticians …
Thanks, FM, that’s the sort of analysis I was looking for. You always have the best sources!
Excerpt from Friday’s report by David Rosenberg, Chief Economist of Gluskin Sheff (via Zero Hedge):
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I’m just wondering where the machine that goes ‘ping!’ is.
Paul Krugman also has a trenchant general discussion of the political motivations behind bizarre posts like Frank’s here, in “Truth about jobs.”
The obvious rebuttals to Frank’s fringe lunacy involve
(1) the recognition that people give up looking for work and thus paradoxically drop off the charts of those technically unemployed;
(2) seasonal adjustments often add or subtract large numbers of workers because the BLS survey numbers are estimates which get firmed up later;
(3) statistical fluctuations are common in a large modern industrial economy, so workers can and do suddenly appear and disappear from the workforce the same reason lots of people can suddenly come into a convenience store at once;
(4) talking about “more granular looks at the data” and “the raw data” is arrant nonsense. Raw data often changes drastically because it includes estimates going forward, which then must be revised a month or two later. The reason all raw data includes estimates is that there is a huge amount of data and typically a lag time twixt when the data gets collected and when it’s all added up and verified, and for very large datasets this lag time can be months.
So if we want to avoid constantly looking at the rearview mirror and only being able to discuss what the unemployment rate was 6 months ago, we must include some estimates, including seasonal variations which must be mathematically extrapolated from past years and used to modify this year’s numbers. As for example hiring spikes around the holidays etc. “Raw data” which does not include these mathematically extrapolated modifications will wind up being much inaccurate than the smoothed data.
(5) the time series on which these data are based exhibit statistical characterstics that are known, and whose variance and x-squared values and Student t-distributions and ANOVA parameters can be compared with the parameters of recent past datasets to determine sampling bias, bimodal distributions, and other sources of error. Once identified, these sources of error can then be corrected by deconvolution, Kalman filtering, Runge-Kutte point-and-shoot parameter tracking of the nonlinear partial differential equations representing the underlying dynamics of the economic model, and so on.
In short, there’s a huge amount of math and modeling going on under the surface of a simple set of numbers like these. If you’re not familiar with working ANOVA analysis of massive time series subject to lots of statistical noise, you won’t be aware that random matrix theory tells us there is a finite likelihood that parameters which appear with a highly probabilty from the xi-squared tests to be correlated at a level of statistical significance (95%) actually aren’t. If you don’t understand Bayes’ Theorem, you won’t have any idea how these numbers were arrived at or what the modifications to the raw data mean and why they’re necessary.
Frank seems typical of America’s innumerate citizenry. The “numbers just don’t feel right,” so we must reject them.
As commenter David Jones memorably remarked “That’s such a disturbing image I want to question it.” Shorter version: if reality alarms us, deny it!
History shows that such escapades do not end well.