ON: Despicable Journalism, Confusing of Cause vs. Effect

Was rather puzzled by all this, until I quickly realized the article is from Huffington Post. A far Left mouthpiece. There is little analysis, though they touched on it very briefly, only a statement of one fact then a march to the desired "outcome". That's not "news," rather enemy propaganda.

Let's go to bottom-line: Based on that article, assuming the facts are straight, all I conclude is that Black Women are not recovering as well as other groups in employment, and that this must be "solved" by legislation. No mention is made of root-cause analysis. No facts are provided about experience, education levels, incomes, geography, or other relevant attributes of OTHER races/genders and their employment recovery stats, and what that might mean in relation to all groups. Individually, and collectively.

Steps leading to desired Outcomes need to be determined by mathematically and scientifically-rigorous analysis. No matter where they lead, nor how they make people "feel." Stick to the facts. I need to understand that my tax dollars are being spent moving the greater good forward, not pouring money at special interest with no accountability.

PS: the Real Analysis:

Poor reporting, with an agenda, grows very tiresome after awhile. I'd find it funny, except people actually both read and believe the outcome...always in-line with politics of the day... needs to be "solved" by legislation vs. actually doing some rigorous root-cause analysis. Then, and only then, should we (the People) consider collective steps to address the real issue(s) CAUSING outcomes.

If there is data suggesting a disparate employment recovery rate for Black Women, where then is the full data set, w/analysis, for all racial groups? Fact tables, pivoted on, say, ten or so attributes (dimensions), which may include: geographic location, race, education level, age, number of children, other important things that are not necessarily numeric but can be tabulated.

That is called a data cube: swivel the cube on different dimensions, virtually, to view the various outcomes driven by the underlying facts (numbers). Not supposition, not desired outcomes: facts.

For each outcome, or groups of outcomes, from the above do the ANALYSIS and summarize in layman's terms. The facts will lead to various logical deductions, or show gaps requiring more facts before we can analyze and suggest remedies.

Example analysis and conclusions: spurious, for illustration only:

WHITE MEN saw an employment recovery of 2.1% overall, quarter over quarter, from this time in 2013. Recovery was greatest in the Northeast (3.1%), Southwest (4.3%). Age group 60-71 saw negative growth, 45-60 5%, etc. Job types engineer, scientist, and mathematician...dominated by White Men...saw greatest overall growth. Breakdown as-follows: (data).

ANALYSIS AND CONCLUSIONS: White Men in the Pacific Northwest, Master's degree and higher, in STEM fields, had a great 2013-4, with overall job growth of 11.5% YoY. In the Southeast, White Men age 16-24 with less than High School education saw an increasing decline, from 21.5 to 30% unemployment. Those who are employed are primarily in Service, food (40%), delivery (20%), catering (28%) and remainder in other.

Clearly, for White Men, job growth is great in such tech centers as Seattle, San Jose, and greater Los Angeles, in roles requiring X education and Y years of experience and of Z age group.

(etc.)

Then do the same for other major ethnic groups, and by-gender. Wouldn't it follow that analysis would definitely stimulate interesting debate?

The article quoted is in-fact part-and-parcel to gibberish swirling around regarding the "problem" of racial and gender makeup of certain large tech firms' employees. One or more of whom elected to release detailed employment statistics, recently. Which, not-surprisingly, created a public outcry. Suggesting, though not proving, that certain aggrieved groups were somehow underrepresented in employment. Always the same, which is probably why said-companies were reluctant to release the data in the first place. Many (most) still haven't, and shouldn't, unless compelled by law.

Lost in the debate was that such firms hire based on performance and availability, not some "affirmative action" playbook. Economics and competitiveness follow no such artificial rules, obviously. Attempting to permanently affect economics by-fiat (legislation, executive decree, populist sentiment) invariably ends in farce.

To wit: no one can, or possibly could, explain how equilibrating on racial "outcomes" independent of ability correlates positively to increased business effectiveness.