Conspiracy Theories About Jobs Numbers

Every month, the Labor Department releases figures on the current state of the economy, and every month, without fail, malcontents take to the media to say that the numbers are phony. For example, Paul Singer, a hedge fund billionaire and prominent donor to Republican candidates, recently wrote to his investors, “Nobody can predict how long governments can get away with fake growth, fake money, fake jobs, fake financial stability, fake inflation numbers and fake income growth.”

If you have read my books, you know that I rely heavily on data from the U.S. Department of Labor, so it’s obvious that I don’t share Singer’s skepticism. The data analysts at DOL are career civil servants without a stake in enhancing the reputation of whoever happens to be in power. More important, if there were a vast conspiracy to conceal the true state of the economy, surely someone would play the Edward Snowden role and blow the whistle.

It’s true that the unemployment figure does not indicate the actual percentage of people out of work, because it covers only those who are looking for work and ignores those who have given up. But how many times does the public need to be told this? The Labor Department never pretended otherwise. The numbers are what they are.

Last week, a friend of mine whom I have known since seventh grade pointed out an example of the absurdity of the conspiracy theorists. He wrote on Facebook,

You may have heard about the jobs report that came out last week. The mainstream media generally received it as good but not super—248,000 jobs added (200,000 is order-of-magnitude break-even). Now, this is seasonally adjusted, and October is a strong month for jobs, so it was adjusted down, by a lot, in fact—800,000 or so. If the adjustment formula from last year had been used, growth would have been around 100,000 more. So sure enough, the daily stock market letter I look at has rumblings about government fraud to report the low number. The logic is that acknowledging the true extent of expanding employment would have undercut the case for low interest rates.

He goes on to note another competing conspiracy theory, regarding the other number that was reported—the unemployment percentage:

On the other hand, following links from the newsletter, I find another deep thinker saying that the reported unemployment rate is too low because of fraudulent underestimation of the potential work force. The reason for that fraud would be to minimize the appearance of troubles. So is the current employment picture better or worse than the government is telling us?

As my friend observes in conclusion, these market-watchers accuse the government of “two separate conspiracies working at cross-purposes.” Surely the government can’t be lying to make the economy look both better and worse than it really is.

Writing in The New York Times, Floyd Norris acknowledges that sometimes the reported economic indicators are inaccurate, but this is not because of a conspiracy to make the administration in power look better. Rather, it’s because when Labor’s economic analysts adjust figures, they tend to extrapolate from current trends. If the economy reverses direction, this tendency can cause economists to get the numbers wrong. When the economy goes into free-fall, they may overstate performance, but when it bottoms out and starts to improve, they may understate the situation. He gives the example of a 2012 Twitter comment by Jack Welch, the former chief executive of General Electric, who said that the reported decline in unemployment at that time was “unbelievable.” (This was in October of a presidential election year.) In fact, the figures later had to be readjusted to reflect the fact that the emerging economic recovery was actually more rapid than the initial figures indicated. (Incidentally, following Welch’s comment on Twitter, several other messages pointed out Welch’s reputation for manipulating the numbers reported for GE Capital.)

Norris recommends skepticism in situations where there are “rapid changes in any indicator, particularly if other indicators do not show similar changes.” But he cautions that we should “separate reality from ideology.”

When Robots Create a Need for Human Workers

Yesterday a Facebook friend of mine wrote about an “eerie and creepy” experience. An advertisement had just appeared in the margin of the Facebook page she was reading, showing a tee-shirt with the lettering, “Just a California Girl in a New Jersey World.” It is no coincidence that my friend is a native of California and now lives in Flemington, New Jersey. Facebook obviously sold this user-profile information to advertisers, one of whom found a way to develop a highly personalized product.

But my friend did not buy the tee-shirt, and if enough other targets of this pitch find their personalized tee-shirt unappealing, the manufacturer will lose money on the ad campaign (plus the costs of tooling up to produce the tee-shirts; the manufacturer surely doesn’t have an inventory of shirts for every possible two-state match-up). What this tells me is that although Facebook’s data-gathering (a robotic function) makes it possible to create highly personalized products, the actual creation of products that people will buy remains a human function. In the brave new world of Big Data, human creativity is still needed to make a sale.

Something very similar became clear in this month’s congressional elections. As The New York Times pointed out,

Modern political campaigns home in on their key voters with drone-like precision, down to the smallest niche — like Prius-driving single women in Northern Virginia who care about energy issues. They compile hundreds of pieces of data on individuals, from party registration to pet ownership to favorite TV shows. And they can reach people through Facebook, Pandora, Twitter, YouTube or cable television.

The only problem: They do not have enough messages for them all.

The Big Data era of politics has left some campaigns drowning in their own sophisticated advances. They simply cannot produce enough new, effective messages to keep up with the surgical targeting that the data and analytics now allow.

…Or, as Joe Rospars, the founder of the Democratic digital agency and technology firm Blue State Digital, put it, “The science is ahead of the art.” An analytics team can help a campaign make “a much more targeted buy,” he explained, but that alone will not offer a particularly efficient return on investment if the ad is still “just a white guy in a suit.”

As a result, the people who design the advertisements for electoral campaigns end up trying to tease out certain large slices of voters with something in common, such as “soccer moms” or “angry white males.” The campaigns do not have enough creative people to craft the highly personalized ads that should be possible given data-analysis tools.

On a recent broadcast of NPR’s “On the Media” (sorry, I can’t remember which date), I found another example of the limitations of technology. You may remember that YouTube moved quickly to take down the videos of the recent beheadings in Syria. You may not know that YouTube takes down many other videos because of pornographic or sadistic content, and so do most photo-posting sites, such as Flickr and Photobucket. Computer technology makes it easy to post photos and videos, and to make them searchable by keywords, but the explosion of content that has appeared on the Web requires human eyes to decide which photos and videos violate site policies.

One indication of how subtle the human decision-making must be is the fact that photo sites prefer to offshore this work to the Philippines rather than to India, where the workers are lower-paid. The reason is that Filipino workers have a better understanding of American culture and therefore can decide whether (for example) a shot of someone in a bikini is too revealing, whereas an Indian worker might reject every bikini shot.

The takeaway is that technology sometimes creates a need for more human workers, and not just those employed in creating, manufacturing, or repairing the technology. It sometimes creates opportunities for work that requires great creativity or subtle judgments. Robots may be driving cars, but they are not yet metaphorically in the driver’s seat.

High-Paying Occupations with a Few Superstars

My most recent book, Your Guide to High-Paying Careers, is my first for my new publisher, Meyer & Meyer. Here is a brief excerpt, giving you a peek at some occupations where the sky is the earnings limit--for a few outstanding workers.

Have you ever fantasized about winning the lottery jackpot? Some occupations have a few jackpot positions that pay extremely well. You’re certainly familiar with movie stars who earn the millions that most struggling actors can only dream about. The radio plays songs by musical superstars who bring home more than the combined earnings of 100 bar bands.
 

Among the occupations included in this book, some offer extremely high earning opportunities for a relatively small subset of workers. You can’t tell which occupations these are by looking at the median wage figure or even at the range of the middle 50 percent of earners. But I have a way of identifying these occupations for you.

Imagine this situation: Five friends are sitting around a restaurant table having lunch. They all earn roughly the same amount:

Person	Earnings
Joe	$50,000
Lydia	$51,000
Mateo	$52,000	← median
Isabella	$53,000
Mike	$54,000

For the group, the median earnings figure is $52,000 (half earn more, half earn less). If you calculate the mean earnings (add them all up and divide by 5), you’ll get the same figure, $52,000. But let’s say Mike gets a phone call telling him that he was just promoted to vice president and is now earning $150,000. Note that the median for the group has not changed, but the mean has jumped to $71,200. Because there is now one superstar earner in the group, the mean is now 37 percent higher than the median.

For the 173 occupations in this book, I calculated the difference between the median earnings and the mean earnings. (The BLS reports both figures.) The following list includes those occupations in which the mean is at least 15 percent higher than the median. They are ordered by how much the mean exceeds the median. For each occupation, I list both earnings figures.

What might cause you to be among the highest of the high-paid workers? Here are some possible reasons:

• You have an outstanding talent. Maybe you were born with some ability that few other people have.
• Through hard work or study, you have developed outstanding skills. Whether these are physical or mental skills, they can put you ahead of the pack.
• You use mass media to reach a very large paying audience. Think about how much more a TV chef can earn compared to a one-restaurant chef.
• You find a specialization or a geographic location that causes you to be in high demand but have no competition. This advantage may be only temporary, but you may be able to command high earnings as long as you are the go-to person for your narrow field or community.
• You go into management.

Whether you look at the following list as a collection of nice fantasies or as possible roadmaps to your future, the list makes for interesting reading.

High-Paying Occupations with a Few Superstars

	Title	Median Earnings	Mean Earnings
1.	Securities, Commodities, and Financial Services Sales Agents	$71,720	$100,910
2.	Agents and Business Managers of Artists, Performers, and Athletes	$63,370	$88,620
3.	Real Estate Brokers	$58,350	$80,220
4.	Personal Financial Advisors	$67,520	$90,820
5.	Producers and Directors	$71,350	$92,390
6.	Health Specialties Teachers, Postsecondary	$81,140	$100,370
7.	Advertising and Promotions Managers	$88,590	$107,060
8.	General and Operations Managers	$95,440	$114,850
9.	Chiropractors	$66,160	$79,550
10.	Art, Drama, and Music Teachers, Postsecondary	$62,160	$73,340
11.	Health Diagnosing and Treating Practitioners, All Other	$72,710	$85,740
12.	Loan Officers	$59,820	$70,350
13.	First-Line Supervisors of Non-Retail Sales Workers	$70,060	$82,320
14.	Geoscientists, Except Hydrologists and Geographers	$90,890	$106,780
15.	Biological Science Teachers, Postsecondary	$74,180	$87,060
16.	Art Directors	$80,880	$94,260
17.	Business Teachers, Postsecondary	$73,660	$85,730
18.	Financial Analysts	$76,950	$89,410
19.	Law Teachers, Postsecondary	$99,950	$115,550
20.	Area, Ethnic, and Cultural Studies Teachers, Postsecondary	$67,360	$77,690
21.	Lawyers	$113,530	$130,880