Tuesday, December 17, 2013
Statistical Impact of Disproportionate Age Discrimination at Lawrence Livermore Lab
Statistical Impact of Disproportionate Age Discrimination at Lawrence Livermore Lab
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The plaintiffs in Phase II of our trial Andrews v. Lawrence Livermore National Security, LLC (LLNS) have suffered emotional distress, humiliation, and feelings of inadequacy as a result of their illegal layoffs from Lawrence Livermore Lab (LLNL) in 2008. The disproportionate layoff of workers age 40+, leading to disparate age discrimination at LLNL, can be illustrated through statistical analysis. To that end, plaintiff’s expert witness, statistics Professor William Lepowsky, came to the conclusion that the odds of so many older employees, over age 40, being laid off were “1 in 1,091,000.” The emotional and statistical significance of this layoff are at the heart of our case.
Andrews vs. Lawrence Livermore National Security, LLC is a unique case that began with Judge Robert Freedman separating the trial into two Phases. Phase I focused on breach of contract and breach of implied covenant of good faith and fair dealing at LLNL. Our plaintiffs prevailed in Phase I with an award of $2.7 million for plaintiffs Elaine Andrews, Marian Barraza, Mario Jimenez, Greg Olsen, and James “Rocky” Torrice.
Statistician: Professor William Lepowsky
Professor Lepowsky has taught statistics and mathematics for 45 years at Laney College in Oakland, California. He is not a professional expert witness, like the statistician hired by LLNS defense team, but rather a professional educator with an esteemed background.
Professor William Lepowsky’s qualifications include:
B.A., Harvard College 1967; Major Mathematics
M.A., U.C. Berkeley 1968; Department Mathematics (graduated summa cum laude)
M.A., U.C. Berkeley 1976; Department Statistics
Qualified as expert witness to testify in the California court system and the Federal courts
“Statistical Significance” Key to Disproportionate Age Discrimination
One of the most important aspects of Andrews v. Lawrence Livermore National Security, LLC is the utilization of statistical analysis brought forth by Professor Lepowsky. Professor Lepowsky’s statistical analysis illustrates that LLNS discriminated against older workers, 40+, during the layoff in 2008. To this end Professor Lepowsky utilizes “statistical significance” the probability that an effect is not likely due to just chance alone.
Lawrence Livermore Lab’s Faulty Layoff Unit Statistical Analysis
The basis for LLNL’s statistical argument was their creation called the Layoff Unit. 273 Layoff Units were created by LLNS line managers, the decision makers in the layoff process, as a means of grouping employees. Layoff Units were not recognized within the rules for layoffs or within the plaintiff’s contracts. According to Professor Lepowsky, the Layoff Units do not accurately represent the layoff in 2008. A few of the characteristics of LLNS Layoff Units:
Nearly 20% of the 273 Layoff Units (48) had only one employee
More than 25% of the 273 Layoff Units (72) had only one or two employees
Nearly 50% of the 273 Layoff Units (125) had five or less employees
30% of the employees that were classified in Layoff Units were assigned to 1% of the Layoff Units
Statistical Analysis Process
In Professor Lepowsky’s statistical analysis, Layoff Units were not used. Instead, Professor Lepowsky utilized the following process in his statistical analysis:
Workforce: 40% of the workforce was not eligible for layoff. These “excluded” workers were chosen solely by LLNS line managers. Professor Lepowsky identified who could be laid off
Comparison: compare “like” employee positions such as physicists with physicists and engineers with engineers
Where Workforce Worked: identify where the workers work and which line managers they worked for at the lab
Job Code: group workers by the same job code
Classifications: group and classify workers by directorate, division, or department
Vital to the analysis is the fact that every employee analyzed by Professor Lepowsky is tied to a decision maker, line manager at the lab. The disparity of sizes of the Layoff Units, used by LLNS in their analysis, was a major reason why Professor Lepowsky did not accept the Layoff Unit analysis. The importance being the fact that Layoff Units did not compare employees with like characteristics.
Statistical Significance Points to Age Discrimination
The following are key statistics and information that support the assertion that disproportionate age discrimination was prevalent during the 2008 Lab layoff. According to Professor Lepowsky, the exclusion process favored employees under the age of 40 to a statistically significant degree and it hurt employees over the age of 40 to a statistically significant degree. The statistical impact is evident within the numbers.
Excluded Employees: line managers chose who would be excluded or safe from the layoff. Of those excluded as of May 22, 2008: 54.1% were 39 years old or younger and 40.6% were 40 years old or older
Excluded Employees Standard Deviation: of all employees analyzed by directorate, division, or department at all levels the layoff had a statistically significant impact on employees over the age of 40. Ranging from 3.34 to 4.09 well above the agreed upon 2.0 Standard Deviation
Layoff Policy: the “different employees, different policies” assertion by LLNS is not correct. There was one layoff with specific rules that were broken by LLNS
1 in 645,000: the probability (chance) that older workers in Directorate and Job Code were “over selected” for layoff if the process of layoff equally impacted young and older workers. Equal to 4.66 Standard Deviations
1 in 1,091,000: the probability that older workers in Division and Job Code were “over selected” for layoff. Equal to 4.77 Standard Deviations
1 in 76,000: the probability that older workers in Department and Job Code were “over selected” for layoff. Equal to 4.20 Standard Deviations
Termination Percentages: 6.6% of employees 39 and younger were terminated vs employees 40 and older who were terminated at a rate of 11.5%
In the final analysis according to Professor Lepowsky, “The disparity between old and young in selection rates (for layoff) is statistically significant.”
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Gwilliam, Ivary, Chiosso, Cavalli, & Brewer
If you are interested in information regarding this case or If you suspect that you, have been the victim of workplace age discrimination, wrongful termination, harassment, or retaliation please contact attorney J. Gary Gwilliam or attorney Randall E. Strauss of the law firm of Gwilliam, Ivary, Chiosso, Cavalli, & Brewer at (510) 832-5411 ext. 233 or GGwilliam@giccb.com
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22 comments:
“The disparity between old and young in selection rates (for layoff) is statistically significant.”
Oh...ya, everyone knows that and that by the way was the point. What you know does not matter, if you are good at the job does not matter, it is all about the profit for the corporation. Look at at this way... you go corporate and say it saves a lot of money...it does not and instead cost a lot more. You ask your your self does it matter what people at you corporation do...answer... hell no. So what to do? You have to get rid of people and every person you fire has as 0.1 percent chance of suing. If you fire 100 old people that is less than firing 150 random people to make it clear 0.01X100 < 0.01X150. So no matter what we win! You see firing 150 random people will still get same fraction of lawsuits just as likely as 100 old people.
It is called math you bitches! We are here to make a buck not to serve the United States. This lawsuit was all factored in and part of the plan. LANS....WINS you ....LOSE! Hell ya PBI's baby! Now can a single one of ya all say it was better with UC? I doon't think so.
The point is that the statistics strongly supports the accusation that LLNS engaged in illegal discriminatory practices. Whether you like the law or not, it's in the books, and LLNS is not above the law.
So what! Where are the lawyers?
"Lies, damned lies, and statistics" is a phrase describing the persuasive power of numbers, particularly the use of statistics to bolster weak arguments. Attributed to Mark Twain (among others).
I think it's "fitting" that this LLNS debacle is falling on Knapp"s big and broad shoulders. It will give him a feel for the LANL Director job when he starts his own discriminatory "ways". He clearly exercised these while at LANL.
Endemic corruption
LLNS committed a crime. The lawyers are there with the plaintiffs. The statistics are out in the open to be challenged. Absence of a credible challenge, it will support the assertion that LLNS committed a crime. Yes, a CRIME.
The correct response to statistical evidence is to refute the premise or the data. But instead, we just got random ramblings, generalizations and truisms. That tells me that the statistical evidence is still pretty damning against LLNS.
Will LLNS managers be held individually accountable as detailed in LLNS policy?
No because there is no paper trail. They know never to put there prejudice laden decisions in writing because it exposes them to scrutiny. In fact this is how they perpetuate bad business practices. Bad bad bad
"... They know never to put there prejudice laden decisions in writing because it exposes them to scrutiny. In fact this is how they perpetuate bad business practices..."
If unlawful employment actions were taken, perhaps LLNS managers should be thought of as a conspiring entity and held accountable as a group.
At what point does the OFCCP, DFEH, or NNSA Livermore Field Office say enough LLNS management, time to correct your unlawful employment practices?
Doesn't the NNSA Livermore Field Office have contract extension "report card" authority?
One has to wonder what is going on in the NNSA field office... People asleep on the job? Or maybe in bed with the lab...
then again, statistical evidence shows that 98% of lawyers (like real estate agents) are slimeballs. I feel bad for the plantiffs - caught between the lawyers and LLNS....
Bad statistical analysis and interpretation can be refuted... still waiting.
The big problem is that the lab DID willfully commit unlawful discriminatory actions. You don't need the statistics to prove it. It's just extra icing on the cake.
Personnel were laid off in obsolete projects that had no funding, or insufficient funding at the UC LLNS transition. Unfortunately, these programs also had some of the oldest employees. That's my recollection, as unfair as it was.
I don't thinks there was overt age discrimination among most managers. However, today it does exist as I know for a fact based on personal discussions with senior managers.
They put the youngest and often most inexperienced people onto projects expected to survive as a way of priming the pump for eliminating the highest cost older workers when time comes for program cuts and lay-offs. We all know this is intentional. Don't bother trying to blow hot air up our arses by saying that it was all coincidence.
When older employees don't go out of their way to "push" to work on the new projects - don't go to the meetings because they are "busy" or "not invited" - or are perceived to not care because they show up for work at noon - then they set themselves up for problems when the older programs get cut back.
Those plaintiffs had consistently good performance appraisals. No mention of such systemic problems like the way you are trying to portray older workers. So I'm not sure where you get your drivel from. You are probably just spouting off your own prejudices. In your own version of reality, are blacks and Hispanics also problem employees in the same way that older workers are?
LLNL and Sandia management are like that.. They start with the conclusion that they are compelled to arrive at first, then they alter the facts and severely distort reality in order to arrive at that conclusion. Come to think of it, some of their scientists apply that same paradigm to their so-called "research". It's a perverted form of deductive logic where the conclusion is first confirmed and the premises are "flexible"
HA HA HAHAHA - LOSERS!!!
Pages of meaningless stats get shot down by the courts instantly.
I love it when ambulance-chasing lawyers get run over.
HAHAHAHAHAHA
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