Big Data, Plainly Spoken (aka Numbers Rule Your World)

TSA can now count its workers as paid

Last week, I pointed out that the U.S. payroll survey uses the following accounting rule: 

The Labor Department... determines the number of people on nonfarm payrolls by asking employers for a head count during the pay period including the 12th of the month. For many federal employees, that would be the pay period from Jan. 6 to Jan. 19.

Further, one only needs to have worked for one hour during that period to be counted as employed.

This weekend, we heard word that the government shutdown has no end in sight. 

On Sunday (Jan 13), we heard more news - that the TSA has decided to issue each TSA worker a one-time one-day pay plus $500 bonus. This means TSA can now report all workers as having been paid during the pay period including the 12th of January.

 

 

The importance of knowing how things are counted

Yesterday, on the sister blog, I posted a pair of charts that digs into how the unemployment rate is computed. This number has been in the news a lot because we supposedly have a super-tight labor market, the lowest unemployment rate since the peak of the tech boom in 2000! But the story is much more complex if one understands how unemployment levels are counted.

Another reason why counting unemployment is in the news is because of the government shutdown. And the Wall Street Journal has a nice (but rather convoluted) article about what that means for the unemployment number. This is where you can see how politics enter into the statistics - it's via rules that determine what gets counted and what doesn't.

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As I pointed out in Numbersense (link), the Labor Department uses a specific definition of employment, and here is how the WSJ describes it:

The Labor Department... determines the number of people on nonfarm payrolls by asking employers for a head count during the pay period including the 12th of the month. For many federal employees, that would be the pay period from Jan. 6 to Jan. 19.

In other words, someone just needs to have worked at least one hour during that pay period to be considered "employed".

Based on that definition, if the government shutdown extends beyond Jan 19, then there should be a big negative impact on the unemployment rate.

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But wait - politicians of neither party would want that to happen (in case they are the ones in power at the moment).

So what do we learn from the WSJ reporter? This:

even if the shutdown extends beyond the pay period, workers would be counted if legislation is enacted that requires agencies to pay employees for what they would have earned had there been no shutdown, a spokesman for the Labor Department’s Bureau of Labor Statistics said. Such practice would be consistent with how the department accounted for workers during previously lengthy shutdowns.

The reason given for this is precedence. But that actually means that the unemployment rate in that scenario would not accurately portray the state of employment in the U.S.

An economist cited in the article further explained that the following could also happen:

some federal employees initially furloughed have been called back to work and some are being paid with supplemental funds.

Since it only takes one hour of work during that specific pay period to be counted as employed, it's not hard to come up with ideas about how to turn these not-working people into employed people.

As a data analyst, there is no getting around learning all these nuances if you want to interpret data properly. It's the nature of our work.

Lies, damned lies, and tech

In the second half of 2018, in the aftermath of the Cambridge Analytica-Facebook story, the news media have broken out of a stupor, and started realizing that the world of the Web and mobile is filled with fake data. I've written about this problem quite a bit. For example, my 2017 piece on the birth of the "fake news business" is still worth your time.

I also urged data analysts to recognize the amount of fake data that we are analyzing while looking the other way. This post describes some real examples of fake data discovered by journalists. Even earlier, in 2015, I authored a piece for HBR discussing Augustine Fou's work on uncovering ad fraud - driven by automated bots.

New York magazine just noticed that a lot of Web data are fake. This is a nice piece discussing the current state - it has only gotten worse. Among the trove of lies and damned lies mentioned in this article are fake web traffic, fake websites, fake clicks, fake mouse movements, fake social network accounts, fake cookies, fake video views, fake time on site, fake subscribers, fake video viewers, fake AI assistants, fake Instagram influencers, fake sponsored content (i.e. ads that pretend to be news), fake people, ... One inexplicable omission is the fake product review, which is probably the most commonly encountered species.

The tech industry is driving a lot of this and has not taken the proper actions to contain this problem before it goes out of hand. The fake data problem will evolve into a trust problem; in the last half of 2018, we start to hear rumblings of discontent.

 

 

What's great about Google Maps navigation, how it works, and the role of location data

 

A new video is up on our channel. I explain why Google Maps - and specifically the embedded navigation software - is one of the insanely great products from the Big Data era. (Similiar products are available from GPS providers such as Garmin and TomTom but Google has superior marketing and "pricing".)

How do Big Data power this product behind the scenes? Where do the data come from? Why the same Big Data that bring you insanely great products also threaten your privacy? To learn more, click here.

If you like what we are doing with the videos, let us know by subscribing to our Youtube channel and liking our videos. Youtube plays this game where it withholds goodies - such as the ability to customize the URL of the videos to something you can remember and not a string of random alphabets - until I attain some minimum of subscribers and video plays. This is frustrating to anyone who is not a big brand, and does not want to participate in the fake-data economy.