Marketplace Disses Trump

Kai Ryssdal

Kai Ryssdal

Kai Ryssdal’s editorial comment at the end of “Marketplace” July 26, 2015. Copied and pasted without editing by your faithful correspondent. Over-the-top phrasing, however, is solely the responsibility of Mr. Ryssdal.

A word about the unemployment rate and this election.

This weekend on CNN’s Sunday show State of the Union, an interview between Jake Tapper and Donald Trump Jr. turned to the unemployment rate, about which Trump said: “These are artificial numbers, these are massaged to make the existing economy look good, to make this administration look good, when in fact, it’s a total disaster.”

To use as straightforward a word as possible, that’s a lie.

Reasonable people can and do disagree about the health of the economy, and how to measure the labor market, but the idea that the Bureau of Labor Statistics [BLS] manipulates the monthly unemployment report is without any basis in fact.

It’s at best a fabrication and at worst, and most damaging, a malicious conspiracy theory.

Same thing goes, by the way, for the Republican nominee’s claim that unemployment in this country is at 42 percent.

This isn’t, to quote Jake Tapper, an anti-Trump position or a pro-Clinton position.

It is a pro-truth position.

Mr. Ryssdal has made a fundamental error. He assumes the BLS gathers the data for the unemployment rate. That is not true. BLS outsources data collection to the Bureau of the Census. And, shortly after taking office in his first term, President Obama moved Census from it’s long-time home in the Department of Commerce to the Executive Office of the President. Which means the administration directly runs the critical data collection job for much of the government. (If you’d like to get some idea of the extent to which the federal government relies on Census data, visit http://census.gov and try to find any single piece of data you’re looking for. The sheer quantity of numbers available is overwhelming. I say this as someone who has spent many, many hours trying to track down data on that site.)

In any case, I’ve written about this before. And I have to remind everyone that John Crudele wrote a series of articles detailing how the data was being faked.  I compiled the articles into a single pdf file which you can read by clicking here.

Having written all this, I have to add one disclaimer.  I do not support Mr. Trump’s candidacy.  I am, in fact, #nevertrump.




My Refusal to Use U.S. Government Data Has Been Extended Indefinitely

RIP Government Data

(click for larger image)

In early 2014 I wrote that I would not use government data again for three more years.  I have now extended that deadline indefinitely.  Neither of the candidates for president inspires more confidence in the data than President Obama.  I have updated the image at the top right of the home page to reflect this fact.  Here’s the updated version →

I will shortly post a companion article about Kai Ryssdal’s failure to understand the issues with government data.




Why I Will Not Use U.S. Government Data for Three More Years

Fake Jobs Are Not Real Jobs

Fake Jobs Are Not Real Jobs

[Update April 20, 2014. This article was featured in James Taranto’s “Best of the Web Today” column in the Wall Street Journal.  Click here to see it.]

[Update May 18, 2014.  In response to a comment from larsschall.com, I have added a paragraph at the end explaining my three year time limit.]

For quite a few years my lovely wife has refused to believe any economic data from the U.S. government.  Until recently, I have been a staunch defender of the statisticians and economists who work in various branches of the government.  The four I use most often are the Bureau of Economic Analysis (Commerce), the Bureau of Labor Statistics (Labor), the Treasury Department, and the Census Bureau (formerly in Commerce, stay tuned).

Much of the data supplied by these departments rely on Census to perform the actual survey work.  Census has the tools for this and they have been in the survey business for a long time.  Unfortunately, that also creates a single point of vulnerability.  As far as anyone can tell, the Obama administration successfully exploited this vulnerability and produced false survey data.  The falsification itself was not at all sophisticated.  One or more Census employees simply made up numbers.  John Crudele of the New York Post has covered this extensively.  In yet another measure of how bad the “news” industry has become, Mr. Crudele remains almost entirely the sole source of this information.

U.S. Government Data Has Been Faked

I have reluctantly concluded that I cannot believe any numbers emanating from the U.S. government.  The purpose of this article is to explain why I will not use U.S. government data for three more years.  The exception is long-term historical data that is harder to fudge. I remain hopeful that the next occupant of the executive branch will restore integrity to the data.

I have compiled Mr. Crudele’s articles into a single 4.7 mb pdf file.  Click here to download it.  I have also compiled a list of headlines and links from the oldest (November 18) to the most recent as of today (December 16).  I will update this post from time to time as Mr. Crudele provides more evidence.

Those who do not believe the Obama administration is corrupt should look at Mr. Crudele’s articles.  Read carefully and keep an open mind.  What’s in there is devastating.  I am personally heartbroken speaking as someone who has used and relied on this data since 1971.

Why Three Years?

Why three years?  That’s the end of the Obama administration, the same administration that moved control of the Census Bureau from the Department of Commerce to the White House.  I did not intend to make any predictions about what is likely to happen after Mr. Obama leaves the White House.  I will re-evaluate my position on this issue at that time.

Bibliography

Census ‘faked’ 2012 election jobs report
http://nypost.com/2013/11/18/census-faked-2012-election-jobs-report/

House probes Census over ‘fake’ results
http://nypost.com/2013/11/19/house-probes-census-over-fake-results/

Census ‘Fake’gate goes back even further
http://nypost.com/2013/11/21/census-fakegate-goes-back-even-further/

False job numbers: Did the White House know?
http://nypost.com/2013/11/23/cooked-census-reported-to-obama-and-rahm/

Get ready for lies and Labor Department statistics
http://nypost.com/2013/12/02/get-ready-for-lies-and-labor-department-statistics/

So much for Census ‘oversight’
http://nypost.com/2013/12/04/so-much-for-census-oversight/

Warning: Jobless rate may be rigged
http://nypost.com/2013/12/07/warning-jobless-rate-may-be-rigged/

Brooklyn Census Bureau also falsified data in 2010
http://nypost.com/2013/12/16/brooklyn-census-bureau-also-falsified-data-in-2010/




The Explosive Growth of SNAP

The Supplemental Nutrition Assistance Program (SNAP) was called food stamps in its previous incarnation.  Today, recipients get a refillable debit card instead of actual pieces of paper.  Ah, progress.  This article takes a quick look at the explosive growth of SNAP during the Obama Administration.  Data is from a page on the U.S. Department of Agriculture website.  I have also used data on the U.S. consumer price index (CPI) published by the Bureau of Labor Statistics.  (I actually used the handy FRED data tool from the Federal Reserve Bank of St. Louis.  This nifty Excel add-on lets you download and update macroeconomic data series from within Excel.)  As always, my data and methodology is transparent: click here for an Excel 2011 workbook that includes all the data series and graphs.

And explosive is the correct word.  By any measure there has been a steep, sharp increase in SNAP over the past four years.  About the only things that have remained relatively constant are the real per person and per household benefits.

SNAP Consumers Relative to Population

The first indicator is SNAP recipients as a percentage of population.

SNAP recipients as a percentage of population

SNAP recipients as a percentage of population

In 2008, roughly one in ten U.S. people received SNAP assistance.  Today that number is one in every 6.6.  An increase of five percentage points in four years qualifies as explosive growth.

A Bit of Good News

Real per person and per household spending on the program has remained relatively constant (after a spike in early 2009).

Real SNAP spending per person and per household

Real SNAP spending per person and per household

But Other Measures Show Rapid Growth

The number of people and households receiving SNAP assistance have also increased rapidly.’

Number of SNAP recipients

Number of SNAP recipients

That’s a 54.69% increase in people and a 65.27% increase in households over a four year period.  That’s 11.5 percent per year for people and 13.4 percent per year for households.  If employment had grown at 1/4 those rates I’d be a happy economist.

Finally, we need to look at total real SNAP spending.

Real SNAP spending

Real SNAP spending

Conclusion

The numbers speak for themselves.  I can only add a question: is this really the country we want to live in?




The September Jobs Report

It’s been nine days since the September jobs report was released by the Bureau of Labor Statistics.  I’ve been working on a much longer article, but decided to post this abbreviated version to pull together some analysis.

Introduction

Did the U.S. economy really gain 873,000 jobs in September, 2012?  Was the unemployment rate really 7.8%?  Economists have reacted to these numbers with a peculiar mixture of disbelief and defensiveness.  No sane economist believes these numbers represent the current state of the U.S. economy. A quick-and-dirty estimate says that real GDP would have to grow at a 4 – 5% annual rate to add that many jobs.  Actual GDP growth in recent quarters has been below 2.5%.

U.S. Employment

U.S. Employment

But, at the same time, we economists are vehemently defending the statisticians and economists at the Bureau of Labor Statistics (BLS).  (Technical note: we should also be defending the Census Bureau because those folks conduct the “household survey” under contract from BLS.  Technically the household survey is called the current population survey, CPS, while the establishment survey is called current employment statistics, CES.)

Before going any further, I have to say that there’s a good chance that the 873,000 increase in jobs is simply a statistical fluke.  Remember, total employment is estimated using a sample of 60,000 households.  There is a large margin of error.  More details are in the next section of this paper where I look at the numbers and analyze this possibility.

The purpose of this report is to reconcile those two conflicting viewpoints: the jobs number seems unbelievable, but I remain fully confident in the integrity of the number wonks at BLS. And there is also a contribution from the vagaries of the seasonal adjustment process the BLS uses, specifically the treatment of those between ages 20 and 24.  (For those who are interested, Catherine Rampell has an excellent discussion in her blog at the New York Times website (may be behind a paywall). I have included Ms. Rampell’s numbers with a few additions as the last worksheet in the Excel workbook for this report.)

A Quick Look at the Numbers

Before heading into the analysis, I have to mention a few facts about the data.  Since January, 2002 the month-to-month change in employment has had a standard deviation of 356,510 and a mean of only 56,820.  This is a very imprecise number with huge month-to-month volatility.  It has been alleged that the 853,000 employment increase in September, 2012 was the largest increase in 29 years.  Not according to the data: since January, 2002 (and including September, 2012) there have been nine months when employment increased by more than 500,000 and three months in which employment gained more than 750,000 (January, 2012, +847,000 and January, 2003 with a whopping +991,000).  At best, this is the largest increase in 108 months.  The point is that this number moves all over the place.  We shouldn’t take the +873,000 figure any more seriously than, say, the job loss of 1,141,000 in January, 2009.

Month-to-Month Change in U.S. Employment

Month-to-Month Change in U.S. Employment

If you want to stop reading right now, that’s fine with me.  But I think you may find parts of this revealing and/or instructive.  Some parts may even be mildly entertaining.

Statistical Issues and Seasonal Adjustment: Half the Gain

There have been steady changes in the number of people ages 20 − 24 who are employed each September.  According to Ms. Rampell, since 1948 employment in this group fell by an average of 398,000 in September.  In September, 2012, employment of these folks increased by 101,000.  After processing with the standard seasonal adjustment software, the actual seasonally adjusted increase was 368,000, about 42% of the total increase in September.  That leaves 853,000 − 368,000 = 485,000 new jobs still to be explained. Read on.

Note, however, that the 485,000 figure is well within 1.5 standard deviations of the mean since 2002.  That’s a bit more evidence that the number is simply a statistical fluke.

Before going further, it’s important to understand how things are measured.  Much of the following is from the Bureau of Labor Statistics’ Handbook of Methods. (On the BLS website, the handbook is available chapter-by-chapter as separate web links.  Click here to download a copy as a single pdf file. And, as always, my methodology is transparent.  Click here to download the Excel workbook with the gruesome details. This is an Excel 2011 workbook.)

Definitions

An individual in the CPS sample is employed if, “during the reference week, (1) did any work at all as paid employees, worked in their own business or profession or on their own farm, or worked 15 hours or more as unpaid workers in a family-operated enterprise; and (2) all those who did not work but had jobs or businesses from which they were temporarily absent due to illness, bad weather, vacation, childcare problems, labor dispute, maternity or paternity leave, or other family or personal obligations—whether or not they were paid by their employers for the time off and whether or not they were seeking other jobs. Each employed person is counted only once, even if he or she holds more than one job. Included in the total are employed citizens of foreign countries who are residing in the United States, but who are not living on the premises of an embassy. Excluded are persons whose only activity consisted of work around their own home (such as housework, painting, repairing, and so forth) or volunteer work for religious, charitable, and similar organizations.” (BLS Handbook of Methods, chapter 1, p. 6)

An individual who did one hour of work for pay during the reference week counts as employed.  The “reference week” is the week of the month that includes the 12th day.

Individuals in the sample are unemployed if they “1) had no employment during the reference week; 2) were available for work, except for temporary illness; and 3) had made specific efforts, such as contacting employers, to find employment sometime during the 4-week period ending with the reference week. Persons who were waiting to be recalled to a job from which they had been laid off need not have been looking for work to be classified as unemployed.”

This definition, of course, creates the “discouraged worker” phenomenon, along with its impact on the unemployment rate.  (Those interested should read my blog post about the labor force participation rate.)

The labor force is the sum of the number of people employed and the number of people unemployed.  The unemployment rate is the number unemployed divided by the labor force.  So simple, yet with much hidden complexity.

Now that you understand who is employed, who is unemployed, and who is not in the labor force, let’s turn our attention to sampling methodology.

BLS – Census Methodology

There are 60,000 households surveyed each month by the Census Bureau for the Current Population Survey (CPS, usually called the “household survey.”)  That translates into 155,400 individuals using the Census figure of 2.59 people per household.  Of those 155,400 individuals, 78.79% will be age 16 or over.  Even though Census questions those age 15, the only data reported for purposes of the jobs report is on those 16 and over.  With an unemployment rate of 8.11%, we expect 9,932 individuals in the household survey to be unemployed.

Now set a target unemployment rate, say 7.8%.  That implies 9,551 of those surveyed need to be unemployed, a decrease of only 381 people compared to the 7.11% unemployment rate.  Scary, isn’t it? Such are the vagaries of projecting a relatively small sample onto a large population.  (BLS and Census know this.  There are warnings all over their websites and in the BLS Handbook.)

Census conducts the survey during the week of the month that contains the 19th of that month.  Respondents are asked about their employment status for the preceding week, the week that includes the 12th.  There is a rather complicated pattern of rotation in and out of the sample.

Rotation of sample. Part of the sample is changed each month. Each monthly sample is divided into eight representative subsamples or rotation groups. A given rotation group is interviewed for a total of 8 months, divided into two equal periods. The group is in the sample for 4 consecutive months, leaves the sample during the following 8 months, and then returns for another 4 consecutive months. In each monthly sample, 1 of the 8 rotation groups is in the first month of enumeration, another rotation group is in the second month, and so on. (The rotation group in the fifth month of enumeration is returning after an 8-month break.) Under this system, 75 percent of the sample is common from month to month and 50 percent is common from year to year for the same month. This procedure provides a substantial amount of month-to-month and year-to-year overlap in the sample, thus yielding better estimates of change and reducing discontinuities in the series of data without burdening sampled households with an unduly long period of inquiry.” (BLS Handbook of Methods, chapter 1, p. 7)

Conclusion

There’s a reason economists like me make a fairly good living.  We’re willing to dig into the numbers and the underlying assumptions.  If you found this persuasive and/or interesting, you may have the economist gene.




Rounding the Unemployment Rate

President Obama’s chief economic adviser Alan Krueger has gained a certain bit of infamy for rounding the unemployment rate: “More precisely, the rate rose from 8.217% in June to 8.254% in July,”Krueger wrote on the official White House blog, adding: “Acting BLS Commissioner John Galvin noted in his statement that the unemployment rate was ‘essentially unchanged’ from June to July.”

This is stupid.  There are good reasons the unemployment rate is only reported to one decimal place.  Even with a sample of 60,000, there is a statistical estimation error. (See below for a more complete description from the Bureau of Labor Statistics. Contrary to popular opinion, claims for unemployment benefits are not used because those figures ignore unemployed people whose benefits have run out.) Prof. Krueger should know better.  (Of course, he is infamous as the co-author of a badly flawed paper that purported to show increasing the minimum wage lowered the unemployment rate.[1])

This blog is about data.  So let’s look at some recent allegations and see how they stack up. (As always, my methodology and data are transparent.  Click here to download an Excel workbook containing everything I’ve used to support this post.)

One claim is that the unemployment rate would have been much higher if discouraged workers had been included.  Discouraged workers are:

“Persons not in the labor force who want and are available for a job and who have looked for work sometime in the past 12 months (or since the end of their last job if they held one within the past 12 months), but who are not currently looking because they believe there are no jobs available or there are none for which they would qualify.”[2]

In June, 2012 there were 821,000 discouraged workers.  That figure increased to 852,000 in July.  Here’s a summary of the unemployment rates:

 

Jun-12

Jul-12

Unemployment rate as reported

8.217%

8.254%

Unemployment rate including discouraged workers

8.700%

8.755%

So including discouraged workers raises the unemployment rate by about 0.5 percentage points.

But there’s a second category of interest under the heading “Not in labor force.”  That is “marginally attached workers” defined as:

“Persons not in the labor force who want and are available for work, and who have looked for a job sometime in the prior 12 months (or since the end of their last job if they held one within the past 12 months), but were not counted as unemployed because they had not searched for work in the 4 weeks preceding the survey. Discouraged workers are a subset of the marginally attached.”[3]

That’s quite a mouthful.  Let me summarize. Marginally attached workers are really, really discouraged workers.  If we include marginally attached workers, the picture gets considerably worse:[4]

 

Jun-12

Jul-12

Unemployment rate as reported

8.217%

8.254%

Unemployment rate including marginally attached workers

9.662%

9.726%

Look, folks, unemployment rates above 8% are simply unacceptable.  The government screwed up the first stimulus by not shoveling the spending out the door fast enough. They then proceeded to issue so many new regulations (including those issued, proposed, and feared) that businesses have no idea what the future cost of new employees will be.  Meanwhile, the Obama administration insists on increasing taxes on those with incomes over $250,000 per year.  Best estimates are that the proposed new taxes would raise between $35.4 and $37.1 billion.[5]  This amount is pocket lint compared to our $1,500 billion budget deficit.  (If you’re looking for an explanation of the difference between the budget deficit and the government debt, here’s something I wrote a while back.)  Talk about fiddling while the economy burns.

I’ve said it before and it bears repeating: an unemployment rate over 8 percent is no time to raise taxes.  Anybody’s taxes.  Period.  The Democrats should get over their obsession with “fairness” and admit that the tax increase they’re insisting on isn’t worth squat.

 

 



[1] Card, David; Krueger, Alan B., “Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania.” Economics of Labor and Employment Law. Volume 2., 2007, pp. 5-26, Elgar Reference Collection. Economic Approaches to Law, vol. 12.. Cheltenham, U.K. and Northampton, Mass.: Elgar

[2] http://www.bls.gov/bls/glossary.htm/#D Accessed August 5, 2012.

[3] http://www.bls.gov/bls/glossary.htm/#M Accessed August 5, 2012.

[4] Being careful, of course, to not double-count discouraged workers.

[5] Tax Policy Institute (data from Joint Committee on Taxation, U.S. Congress).  Available at http://taxpolicycenter.org/numbers/displayatab.cfm?Docid=3463&DocTypeID=5 accessed August 5, 2012.  Also included in the Excel workbook for this entry.




December Unemployment Rate

 

The December unemployment rate was released this morning.  The measured rate dropped to 8.5%.  As always, a good part of this drop was caused by the ongoing decline of the number of workers in the labor force.  The labor force participation rate dropped sharply to 64.1%.  This is the lowest participation rate since 1983’s 64.0%.  Anyone who argues that the drop in the unemployment rate signals an improving economy should be forced to recycle their Ph.D. in economics into aluminum beer cans.  The graph below tells the gruesome story.

U.S. Labor Force Participation Rate

U.S. Labor Force Participation Rate

Unemployment Rates Revised for 2011

In other news, the Bureau of Labor Statistics issued revisions to the unemployment rate for the twelve months in 2011.  The table below is from the BLS employment report.

Month

As first computed

As revised

Change

January

9.0

9.1

+0.1

February

8.9

9.0

+0.1

March

8.8

8.9

+0.1

April

9.0

9.0

0.0

May

9.1

9.0

-0.1

June

9.2

9.1

-0.1

July

9.1

9.1

0.0

August

9.1

9.1

0.0

September

9.1

9.0

-0.1

October

9.0

8.9

-0.1

November

8.6

8.7

+0.1

As I wrote last month, the November unemployment rate seemed too low.  Frankly, however, I’d chalk that up to a lucky guess rather than any particular skill on my part.

As my fellow economist Dean Baker (co-director, Center for Economic Policy Research) noted in his Twitter feed (@DeanBaker13), some 42,000 of the new jobs were courier jobs, presumably seasonal hiring by FedEx, UPS, and other companies whose business increases sharply during December.  However, the unemployment rate and most other statistics are seasonally adjusted.  Let’s see if we can sort this out.  (Even the BLS commented on this in their report, stating “Employment in transportation and warehousing rose sharply in December (+50,000). Almost all of the gain occurred in the couriers and messengers industry (+42,000); seasonal hiring was particularly strong in December.”  The actual change from November to December in courier employment was an increase of 85,900 workers.  The 42,200 figure cited by Baker uses the seasonally adjusted (SA) numbers.  The 85,900 figure uses data that is not seasonally adjusted (NSA).  Allow me a small digression.

Seasonal Adjustment

Most monthly and quarterly data is seasonally adjusted.  The idea is to adjust for regular cyclical changes that occur every year.  Two big causes of seasonality are holidays (Christmas, Kwanzaa, Hanukkah) and, well, the season.  Ice cream sales rise in the summer.  More products are shipped in November and December.  Seasonal adjustment is designed to remove the regular changes that occur every year.  These days seasonal adjustment is tricky because of the rapid changes in technology, demographics, and the economy itself.  So the real question is how the difference between the seasonally adjusted and not seasonally adjusted figures for December, 2011 compare with historical data.  I’ll add a technical note at the end of this post to explain how to find the exact data.

There’s another issue that came up looking at the data.  The difference between the NSA and SA figures for December rose sharply beginning in 2006.  The average difference between 2001 and 2005 was 15,620.  From 2006 to 2011 the average was 40,200.  Thus the NSA – SA figure more than doubled on average starting in 2006.

Statistical Results

Before I go any further, if you want the data, click here to download an Excel 2003 workbook.  The last tab includes data sources and an explanation of how to extract this data from the bls.gov website.

What we really want to determine is whether the 60,900 difference between NSA and SA in December, 2011 is statistically different from the average.  To do this we use a t-test.  Calculating the standard deviation, then calculating (December – average)/standard deviation gives us a t-statistic.  (Yes, I calculated the sample standard deviation.)  Using data from 2006 – 2011 the t-statistic is 1.68.  Not statistically significant at the five percent level.  According to Stat Trek online the actual significance level is about 7.74%.  Nice grey area result.  (Naturally, using the entire sample period 2001 – 2011, the t-statistic improves to 2.06.  Until someone can explain what happened in 2006, I’m sticking to 1.68.)  For the masochists who like to see the numbers, here they are:

Dec. year Dec. SA (year) Dec. NSA Diff
2011 567.80 628.70 60.90
2010 573.60 623.70 50.10
2009 561.30 594.70 33.40
2008 562.70 595.40 32.70
2007 588.00 620.80 32.80
2006 590.70 622.00 31.30
2005 579.70 591.10 11.40
2004 562.30 572.60 10.30
2003 542.60 568.00 25.40
2002 563.80 580.10 16.30
2001 577.50 592.20 14.70
Average (2001-2005) 15.62
Average (all) 27.91
StdDev (all) 15.98
Average (2006-2011) 40.20
StdDev (2006-2011) 12.35
t (2011 – average all) 2.06
t (2011 – average 2006 and later) 1.68

Another way of looking at this issue is to compare job gains from November to December with job losses from December to January.  We’ll have to wait another month to get the full comparison for this season, but here’s the seasonally adjusted data from 2006 – 2010:

Dec. year Diff Jan – Dec SA Diff Dec – Nov SA
2011 42.20
2010 -48.70 46.30
2009 -40.80 30.10
2008 -5.30 13.40
2007 -4.10 7.00
2006 -8.40 1.30

For the last two years job losses in January have been greater than job gains in December.  And remember, these are seasonally adjusted data.  Unless the labor force continues to shrink at an alarming rate, the unemployment rate for January should tick up to about 8.7%.  Remember, you read it here first.

One more item of note.  The NSA – SA difference in December, 2010 was 50,100.  In the four years before that, the difference was in the neighborhood of 35,000.  Something is going on here.  If I had to guess, I’d speculate that it’s the ongoing steady increase in online shopping.  But wait — these are courier services, not UPS and FedEx.  I look forward to comments from those smarter (and/or more imaginative) than me to clarify this mystery.

Note on Retrieving BLS Data

Start at http://www.bls.gov/data.  Select the link to Employment data, then select the multi-screen data search column in the “Employment, Hours, and Earnings – National” row.  On the next screen, check both the seasonally adjusted and not seasonally adjusted boxes, then click next form.  Scroll down the “Supersector” list box until you see sector 43: Transportation and Warehousing.  Click that, then next form.  In the Datatype list box, click All Employees, Thousands, then click next form.  Scroll down the Industry list box until you find 434920000 Couriers and messengers.  Click that, then next form.  The list box should have two series in it: CES4349200001 and CEU4349200001.  CES is the seasonally adjusted data and CEU is not seasonally adjusted.  Click retrieve data, then download the two Excel files that are generated.

There’s probably an easier way to get this data, but I’ve spent enough time trying to figure out how the BLS hides data.

 

 




Suspicious Data From the BLS

While sifting through the numbers from the November employment report,[1] I ran across what appears to be suspicious data from the BLS.  At the bottom of Table A-11 I found this:

HOUSEHOLD DATA

Table A-11. Unemployed persons by reason for unemployment

[Numbers in thousands]

Reason

Not seasonally adjusted

Seasonally adjusted

Nov.

Oct.

Nov.

Nov.

Oct.

Nov.

2010

2011

2011

2010

2011

2011

UNEMPLOYED AS A PERCENT OF THE

 

CIVILIAN LABOR FORCE

Job losers and persons who completed temporary jobs

5.8

4.8

4.7

6.2

5.2

4.9

Job leavers

0.6

0.7

0.7

0.6

0.7

0.7

Reentrants

2.2

2.2

2.1

2.2

2.2

2.2

New entrants

0.8

0.8

0.8

0.8

0.8

0.8

Source: Bureau of Labor Statistics, http://bls.gov/news.release/empsit.nr0.htm.  Accessed December 2, 2011.

Take a close look at those last three rows.  The numbers are virtually identical.  What’s especially troubling is that the seasonally adjusted figures are the same as those that are not seasonally adjusted.  I would expect a discrepancy in November due to seasonal holiday hiring.

I have no idea whether this is important or even real.  And, yes, I realize the numbers are small relative to the job losers and persons completing temporary jobs.  But this leads me to a bit more doubt about the veracity of the BLS numbers.

 



[1] Yes, I know I have too much time on my hands.  Table 11???