A Labor Force Participation Rate Update

It’s been almost three years since I published my review of the labor force participation rate (LFPR) broken down by age group. This is a labor force participation rate update. Click here to download my Excel workbook.


The LFPR continues its precipitous decline. In 2014 it reached 62.9%, the lowest rate since 1977. That’s 37 years, over one full generation. Here’s what it looks like:

LFPR_over16 A Labor Force Participation Rate Update

The Youngsters

The 16 − 24 age group at least halted the precipitous decline, leveling out at 55%. However, since 2010 this group has the had the lowest LFPR since at leat 1960. The previous low during this period was 1963’s rate of 55.21%.

LFPR_16to24 A Labor Force Participation Rate Update

The 25 − 34 group saw a continuing decline in the LFPR, reaching levels not seen since 1982. At least this group continues to have a high absolute rate, over 80%.

LFPR_25to34 A Labor Force Participation Rate Update

The Middle Aged

The 35 − 44 (“early middle age”) group also stayed above 80%. At 82.18% this is the lowest since 1984.

LFPR_25to34 A Labor Force Participation Rate Update

The 45 − 54 LFPR fell below 80% for the first time since 1988. This is the “prime working age” group. The decline is worrying.

LFPR_45to54 A Labor Force Participation Rate Update

Among the 55 − 64 (“late middle age”) the LFPR seems to have stabilized around 64.5%. This is the first group whose participation has increased during the last seven years.

LFPR_55to64 A Labor Force Participation Rate Update

The Formerly Elderly

You might think these folks are old. But they are staying in the labor force. The BLS now has data on narrower age groups: 65 − 69, 70 − 74, as well as 75 and older.

The 65 − 69 group’s participation has increased steadily since 1994. The LFPR seems to have stabilized around 31.5%. Nevertheless, this is a significant increase over the 20% rates we saw in the 1980’s.

LFPR_65to69 A Labor Force Participation Rate Update

Probably the most startling result is the 70 − 74 age group whose participation rate has nearly doubled since 1987. People are working further into what was once regarded as practically mandatory retirement.

LFPR_70to74 A Labor Force Participation Rate Update

Aggregating the formerly elderly, let’s first look at the group aged 65 and older. In 1960 their LFPR was 20.82%. That rate dropped fairly steadily, reaching a low of 10.77% in 1985. Since then it has increased, approaching the early 1960’s values.

LFPR_over64 A Labor Force Participation Rate Update

The group aged 75 and older has seen their LFPR double since 1987. However, the value of 7.98% in 2014 is small relative to the other groups.

LFPR_over74 A Labor Force Participation Rate Update


I wish I could say there is good news in this report. But the best I can offer is that some trends seem to have leveled off. At this point, however, it’s safe to say the U.S. economy has lost a substantial number of young potential workers who will never make up the ground they have lost.

In some of my other research I’m exploring the possibility that these younger people may actually be working in the “informal sector” (also called the underground economy or hidden economy). It’s possible that the growth of this sector has absorbed some young people, meaning that the reported LFPR’s are lower than the actual participation rate. At this point I do not know whether I will be able to tease even a preliminary answer out of the data. However, there are good reasons to suspect this might be true.

Consider the Affordable Care Act. This plan fundamentally requires young people to subsidize health insurance for older people. One way to avoid this tax is to not report any income (and therefore not file any tax returns). Working off the books for cash probably looks like a pretty good deal to some of these folks.

One of the fundamental ideas of economics is that if you make an activity more costly, people will perform less of that activity. The explosion of regulations emanating from Washington, D.C. has increased the cost of employment in the formal sector (the parts that produce data that is used to measure GDP and national income). It seems likely that at least a few people have chosen to work outside the formal economy. The real issue, of course, is how many have taken this path.

Yet Another Labor Force Participation Rate Error

Zanny Minton Beddoes

Zanny Minton Beddoes (source: Economist.com)

and that’s been one of the really striking things about the U.S. economy in the past few years is that you’ve seen this very big decline in the share of the labor force that is employed, the share of people that are employed. Now partly that is to do with the fact that the population is aging and the baby boomers are aging. As you get older, fewer people work. But a lot of it probably has to do with the weakness of the recovery itself.

 NPR’s “Weekend All Things Considered” featured a story on the state of the U.S. economy. Titled “The Long-Term Unemployed, the State of the Economy, and What Can Be Done,” the show featured a number of guests.  Among those interviewed was Zanny Minton Beddoes of The Economist who, in turn, featured a lovely British accent.  And, naturally, there is yet another labor force participation rate error.

Interestingly, this passage does not appear in the official transcript.

Regular readers (both of you) will remember that I’ve written about the labor force participation rate before.  In fact, retiring baby boomers is exactly the wrong explanation for the declining LFPR.  The main source of the decline is among the 16-34 age group, but the 35-44 age group also shows a small decline.

The historical data is there for anyone who bothers to look.




Where the Vanishing Students Are Going

Today’s New York Times carries a story about declining college enrollments.  The article asks where the vanishing students are going.

College enrollment fell by 2 percent in 2012-2013 compared to 2011-2012.  The Times hypothesizes that these former students have entered the workforce.  However, as I’ve written elsewhere, the labor force participation rate among younger workers (16-35) has been declining (with an update here).  Are these former students being abducted by aliens? Although I think that’s unlikely, it’s more reasonable than simply assuming they have gone out to look for jobs.

My guess is that these students have entered the informal sector (formerly known as the underground economy).  They get paid in cash (actual currency), do not file income taxes and (important) remain invisible to Obamacare.  Since many of the Obamacare taxes and restrictions will be implemented by the Internal Revenue Service, not filing a tax return is an excellent way to avoid the whole train wreck.  (I’ve also written about Obamacare, including predicting the slow-motion train wreck that the program is becoming. For additional readings, click here and here.)

I have no direct evidence to support this hypothesis.  I merely offer it as an alternative to the implausible New York Times story.

Update on the Labor Force Participation Rate

This article is an update on the labor force participation rate (LFPR), adding information through November, 2012. In July I posted a long article about the LFPR. If anything, the situation has gotten worse.  Let’s look at some updated data and graphs.

The two youngest groups (16-24, 25-34) continue their downward trend:

LFPR, ages 16 to 24

LFPR, ages 16 to 24


LFPR, ages 25 to 34

LFPR, ages 25 to 34

The data for 2012 covers the first three quarters of 2012.  The 25-34 age group stayed just about constant compared to 2011: 81.52% in 2012 vs. 81.53% in 2011.  To get some perspective on the 16-24 group, here’s the annual data for the last five years:

Year Participation rate
2000 65.81%
2001 64.57%
2002 63.28%
2003 61.55%
2004 61.13%
2005 60.76%
2006 60.60%
2007 59.42%
2008 58.79%
2009 56.87%
2010 55.18%
2011 54.98%
2012 54.71%

That’s a decrease of 11.11 percentage points.  Once again, it is even clearer that we are losing a generation of workers.  Economists know that getting a job for the first time is one of the best predictors of long-term career success.

There is one canard that needs to be dealt with as soon as possible. Some comments in the press have alleged that the decline in the LFPR has been caused by people in my generation retiring.  Luckily I have the data for the 65 and older age group:

LFPR, ages 65 and older

LFPR, ages 65 and older

Quite the obvious.  Not only are we not retiring, we’re re-entering the labor force.

I’m preparing a much longer piece that will investigate issues around discouraged workers and others marginally attached to the labor force (as the BLS puts it).  Stay tuned.


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:




Unemployment rate as reported



Unemployment rate including discouraged workers



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]




Unemployment rate as reported



Unemployment rate including marginally attached workers



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.

The Labor Force Participation Rate (LFPR): A Closer Look

In this post I’ll take a closer look at the labor force participation rate (LFPR).  Specifically, I will show that the Great Recession has had dreadful consequences for younger workers.  The U.S.  is on the verge of losing a generation of young adults who simply cannot find work and have given up looking.  And, at the same time, I’ll debunk one widespread myth: that the LFPR is declining because baby boomers are retiring.  In fact, a quick look at the data reveals exactly the opposite: the LFPRs for older workers are rising, not falling.

The graphs below were compiled from data at the Bureau of Labor Statistics.  Curiously, the only data I could find on the participation rate did not go back very far.  Luckily, the BLS provides breakdowns of both the civilian noninstitutional labor force and total population by age bracket all the way back to 1960.  So, with the help of our old pal Mr. Excel™, we can grow our own participation rate.  (As always, I value transparency.  Click here to download the Excel workbook with all the data. For technical reasons, this file is in Excel 2007 format.)

Background on the Labor Force Participation Rate

If you’ve been reading this blog for a while you know that I’ve written about the labor force participation rate before.  The definition is simple: the number of people in the labor force divided by total population.  The major complicating factor is the definition of the labor force.  To be in the labor force someone must be either employed or unemployed.  They are counted as employed if they did any work for pay during the previous four weeks.  This is complication number one: many people counted as employed have part-time jobs, but would like to have full-time jobs.  The unemployed are those who did no work for pay during the previous four weeks and are actively seeking a job.  Those who have given up looking for work are not part of the labor force.  A significant increase in the number of these discouraged workers leads to a drop in the labor force participation rate.

The LFPR has been declining since the mid‑1990s.  There was an uptick in the mid‑00’s.  But today the LFPR is at the lowest level since 1988.  This is important.  People who are unemployed for an extended period have increasing difficulty obtaining jobs.  Indeed, there is quite a bit of evidence that their working skills deteriorate.[1]  In general, the longer someone is out of work, the harder it becomes for them to find a job and, if successful, keep the job after returning to work.  After we look at the data, I’ll summarize the research.

The Data

Before we get to the data, a word of warning.  The vertical axes on the graphs have different scales.  Since some groups have LFPRs in the neighborhood of 80% while others are down around 10%, I decided this was the best way to proceed.  If you don’t like what I’ve done, download the Excel file and DIY.

Let’s begin by looking at the LFPR over the last 53 years.

LFPR 1960 - 2011

LFPR 1960 – 2011

The U.S. LFPR hit a low of about 54% in 1975, reflecting baby boomers into the population over age 16.  So let’s look at what’s happened to the 16-24 age group:

LFPR Ages 16 to 24

LFPR Ages 16 to 24

This is where the catastrophe begins.  Between 2000 and 2011 the LFPR for this group fell from about 66% to 55%.  While I would like to believe this reflects more people going to college and even graduate school, I worry that is not the case.  If this precipitous decline is being caused by people dropping out of the labor force because they cannot find work, we are condemning our youngest generation to an increased likelihood of reduced lifetime earnings.

Well, that’s depressing.  Let’s look at ages 25 to 34:

LFPR ages 25 to 34

LFPR ages 25 to 34

The news here is a little better, but only a little.  The LFPR has declined from 85% to about 81%.  At least the decline isn’t as great as it is for younger workers.  The next age group is 25-34:

LFPR ages 35 to 44

LFPR ages 35 to 44

That’s more like it.  The age group 25-44 is actually doing pretty well.  These are the folks who have jobs or are looking aggressively.  What about 45-54?

LFPR ages 45 to 54

LFPR ages 45 to 54

Holding steady at about 82%.

Rather than discussing the last two groups individually, let’s look at both graphs:

LFPR ages 55 to 64

LFPR ages 55 to 64

LFPR ages 65 and Older

LFPR ages 65 and Older

Aha!  People over age 54 are staying in the labor force longer.  I should know — I’m one of them.

This is even more bad news   Younger workers are dropping out of the labor force and, to a certain extent, they are being replaced by older workers hanging on to their jobs.  This does not bode well for the future.  Stay tuned to these pages.  In the coming weeks I’ll look at the duration of unemployment broken down the same way.

Summary of the Economic Research

The most concise summary of the effects of long-term unemployment is from Aaronson, Mazumder, and Schechter (2010):

” As we entered 2010, the average length of an ongoing spell of unemployment in the United States was more than 30 weeks—the longest recorded in the post-World War II era. Remarkably, more than 4 percent of the labor force (that is, over 40 percent of those unemployed) were out of work for more than 26 weeks—we consider these workers to be long-term unemployed. In contrast, the last time unemployment reached 10 percent in the United States, in the early 1980s, the share of the labor force that was long-term unemployed peaked at 2.6 percent. Although there has been a secular rise in long‑term unemployment over the last few decades, the sharp increases that occurred during 2009 appear to be outside of historical norms. Further, this trend may present important implications for the aggregate economy and for macroeconomic policy going forward.

The private cost of losing a job can be sizable. In the short run, lost income is only partly offset by unemployment insurance (UI), making it difficult for some households to manage their financial obligations during spells of unemployment (Gruber, 1997; and Chetty, 2008). In the long run, permanent earnings losses can be large, particularly for those workers who have invested time and resources in acquiring knowledge and skills that are specific to their old job or industry (Jacobson, LaLonde, and Sullivan, 1993; Neal, 1995; Fallick, 1996; and Couch and Placzek, 2010). Health consequences can be severe (Sullivan and von Wachter, 2009). Research even suggests that job loss can lead to negative outcomes among the children of the unemployed (Oreopoulos, Page, and Stevens, 2008) and to an increase in crime (Fougère, Kramarz, and Pouget, 2009).

All of these costs are likely exacerbated as unemployment spells lengthen. The probability of finding a job declines as the length of unemployment increases. Although there is some debate as to exactly what this association reflects, it is certainly plausible that when individuals are out of work longer, their labor market prospects are diminished through lost job skills, depleted job networks, or stigma associated with a long spell of unemployment (Blanchard and Diamond, 1994). For risk-averse households that cannot insure completely against a fall in consumption as they deplete their precautionary savings, the welfare consequences of job loss rise as unemployment duration increases. Welfare implications are particularly severe during periods of high unemployment for individuals with little wealth (Krusell et al., 2008).”[2]


Aaronson, D., B. Mazumder, and S. Schechter (2010), ” What is behind the rise in long-term unemployment?” Economic Perspectives, Federal Reserve Bank of Chicago, 2Q/2010, 23-51.

Blanchard, O. and P. Diamond (1994), ” Ranking, Unemployment Duration, and Wages.” Review of Economic Studies (1994) 61, 417-434.

Thomsen, Stephan L. (2009), “Explaining the Employability Gap of Short-Term and Long-Term Unemployed Persons.” Kyklos, August 2009, v. 62, iss. 3, pp. 448-78.

Ochsen, C. and H. Welsch (2011), “The Social Costs of Unemployment: Accounting for Unemployment Duration.” Applied Economics, November 2011, v. 43, iss. 25-27, pp. 3999-4005.

[1] For examples, see Thomsen, Stephan L (2009); Ochsen, C. and H. Welsch (2011); Blanchard, O., and P. Diamond (1994); and  Aaronson, D., B. Mazumder, and S. Schechter (2010).

[2] Aaronson, et. al., op. cit., p. 23.

January Unemployment

U.S. Labor Force Participation Rate

U.S. Labor Force Participation Rate

The January unemployment rate was released this morning.  Let’s get one thing out of the way right now.  Last month I forecast 8.7% for January.  The actual was 8.3%.  “Forecasting is difficult, especially when it’s about the future.” – Nils Bohr

Economics is known as the dismal science.  You’re about to learn why.  How can a 0.2 percentage point decline in the unemployment rate be bad news?  Read on.

First, every January the BLS updates their data for the civilian non-institutional population to align their data with information from the Census Bureau and other sources.  Guess what?  The bump to population was 1,685 thousand.  At the same time the civilian labor force increased by 508 thousand.  The labor force participation rate fell to 63.73%, the lowest level since 1979.

So apparently there were about 1.7 million folks that BLS thought were dead that were, in fact, alive.  Some have argued that the decrease in the labor force participation rate is partly caused by the retirement of baby boomers.  I wish.  Everyone I know born after World War II is still working or looking for work.

Let’s look at changes between December, 2011 and January, 2012.  The number of people unemployed fell by 339 thousand.  Good news.  And the number employed rose by 847 thousand, also good news.  But 1,177 thousand people dropped out of the labor force.  The employment – population ratio has remained virtually constant at 58.5% for the last three months.  That means the gyrations between employment, unemployment, and labor force dropouts are just about offsetting each other.

When the unemployment rate drops mainly because an additional million people have left the labor force and population estimates are revised … well, let’s just say this report is not the sign of a healthy economy.

As always my work is an open book.  Click here for the most recent Excel workbook.

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.


As first computed

As revised














































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.