#2) There is a difference between being a pure data analyst, and being a person who researches, analyzes and understands the *real world dynamics* behind the data.
#3) The BLS revision is a data skew, that reflects several aspects. However, you must review from a *real world* perspective to understand it.
#4) The BLS data compares reported job hires to the number of workers who filed income taxes.
This is why that specific BLS report -a review of tax records- comes out in August. After taxes are filed; and after the 3 month extension period available to filers.
This is why that specific BLS report -a review of tax records- comes out in August. After taxes are filed; and after the 3 month extension period available to filers.
#5) Over two years, on a national scale, 500k less people have filed income taxes than the hiring companies reported on their hiring reports.
#6) So the question is: Why didn’t those hired people file income taxes?
[These are the missing people, over two years, in the BLS analysis]
[These are the missing people, over two years, in the BLS analysis]
#7) There are several aspects to the answer.
One of them (a significant one) includes “illegal alien” hires in the construction and hospitality industries (restaurants, hotels etc).
One of them (a significant one) includes “illegal alien” hires in the construction and hospitality industries (restaurants, hotels etc).
#8) Another one.... is people who don't file income taxes, who function inside the hidden economy.
Some (good actuaries within the data industry) call this the "black market economy", as a reference. It is a real world and natural dynamic.
Some (good actuaries within the data industry) call this the "black market economy", as a reference. It is a real world and natural dynamic.
#9) An example might be a waiter or waitress who doesn't file taxes because they don't want to report "tips" in their earnings.
This has always existed. This is not unusual.
The difference today is greater data research that shows their shadow, but not their presence.
This has always existed. This is not unusual.
The difference today is greater data research that shows their shadow, but not their presence.
#10) In a VERY hot economy... this group grows; because the overall opportunity grows. It's a simple aspect of ordinary people hiding their income.
If larger income opportunities exist, a larger percentage of the workforce grows in the shadow.
If larger income opportunities exist, a larger percentage of the workforce grows in the shadow.
#11) Now go look at the data, and you’ll see those specific industries are the industries where the hiring reports do not match the income tax reporting results.
#12) It’s not an issue of ‘were those workers hired’?… They were.
The BLS data-skew issue is ‘why didn’t those hired workers file income tax returns’?
The BLS data-skew issue is ‘why didn’t those hired workers file income tax returns’?
#13) That’s the difference between being a pure data analyst, and being an actuary who understands the real world dynamics behind the data. 
#14) Hope that helps.
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