In determining if a Plaintiff made extensive efforts in their job search following their alleged wrongful termination, economic experts should look into several key factors.  Lawyer’s should be very familiar with these factors in order to best represent their client, whether Plaintiff or Defense.

  1. How many jobs has your client applied to and are they similar to the position they were terminated from?  A major point of attack experts should address in their reports will examine if the Plaintiff has performed a sufficiently diligent replacement job search. In Texas, individuals are granted unemployment benefits provided they apply for a minimum of three jobs per week.  This number can be used as the threshold for determining if a Plaintiff has done his or her due diligence in finding replacement employment after the alleged wrongful termination.
  2. How long has the Plaintiff been unemployed?  Widely accepted labor market data from the U.S. Bureau of Labor Statistics can be utilized to determine the average range an individual with a similar job position, in the same job market, would expect to be unemployed.  If the Plaintiff has been unable to find replacement employment within the typical unemployment duration, it is not likely they have performed a sufficient job search.
  3. How many job openings were available in the Plaintiff’s job market at the time of their termination?  Again, data from the U.S. Bureau of Labor Statistics can be utilized to determine job openings per month that the Plaintiff would have been qualified to hold.  In many cases, there are a significant number of job openings in the area the Plaintiff is searching.  Occasionally, a Plaintiff’s job search records will reveal that they have applied to jobs in multiple job markets, sometimes spanning across several states.  To a defense attorney requesting a mitigation analysis, this is music to their ears.  The more markets a Plaintiff makes themselves available to, the more markets experts can include when determining a number of job openings.  This only increases the number of jobs the Plaintiff could have held had they performed a sufficient job search and strengthens the argument that they have not performed such as search.

Local Area Unemployment Statistics (LAUS) is made available by the Bureau of Labor Statistics (BLS), and offers monthly data on employment and unemployment for approximately 7,500 geographic areas. Unemployment rates are available monthly by county, MSA, and state level.

These estimates are key indicators of local economic conditions, and may be compared over time to examine changes in the labor market.

For more information regarding the LAUS, please refer to www.bls.gov/lau

The Job Openings and Labor Turnover Survey  (JOLTS) is a monthly survey conducted by the Bureau of Labor Statistics. JOLTS collects data on total employment, the number of job openings, the number of hires, and the number of separations including quits and layoffs. JOLTS can be used to measure the growth of a particular industry and to better understand labor-market opportunities.

According to the latest release on April 5th, 2016, job hires in the United States increased to 5.4 million in February 2016, while during this same period separations made little change at 5.1 million.

For more information on the JOLTS, please refer to www.bls.gov/jlt

 

The Equal Employment Opportunity (EEO) Census is a tabulation created every ten years for the purpose of serving as an external benchmark for comparing the composition of a company’s workforce to that of the external labor market within a specific geographic area and job category. The EEO Census provides worker counts based on race, ethnicity, gender, age, education level, industry, occupation, and geography. While the raw data is not readily available, 24 tables provide counts for varying cuts of the data.

The EEO Census is most often seen in Affirmative Action Plans and EEO Commission compliance reviews. It is also useful in the litigation setting companies when there are allegations of discrimination.

For more information, please refer to: www.census.gov/hhes/www/eeoindex/eeoindex.html

The American Statistical Association released an important statement and supporting paper concerning the use and interpretation of statistical significance and p-values in statistical research.

pvalues

The American Statistical Associations’ statement notes that the increased quantification of scientific research and a proliferation of large, complex data sets, often referred to as Big Data, has expanded the scope for statistics.  Accordingly, the importance of appropriately chosen techniques, properly conducted analyses, and correct interpretation has also increased.

This statement by the ASA furthers, and in some ways solidifies, the ground roots “counter-statistical significance” movement that many economists and statisticians, such as Steve Zillack and Diedre McCloskey, have been working on for decades.

Real-World-Matters-Statistically-Significant-1024x509

According to the ASA statement “The p-value [and the concept of statistical significance] was never intended to be a substitute for scientific reasoning,” said Ron Wasserstein, the ASA’s executive director. In research analysts use the data to calculate a p-value which shows how consistent the data is with the research hypothesis.  A small p-value is typically interpreted as having a small likelihood of being consistent with the research hypothesis.   In research papers, small p-values are in essence viewed as a ‘good thing’ and according to the ASA statement, are more favored by journal editors for publication.

The ASA statement argues against this approach.  Instead, the ASA statement states that “Well-reasoned statistical arguments contain much more than the value of a single number and whether that number exceeds an arbitrary threshold.”

See:

Ronald L. Wasserstein & Nicole A. Lazar (2016): The ASA’s statement on p-values: context, process, and purpose, The American Statistician, DOI: 10.1080/00031305.2016.1154108

Ziliak, S.T., and McCloskey, D.N. (2008), The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives, Ann Arbor: University of Michigan Press

Ziliak, S.T. (2010), “The Validus Medicus and a New Gold Standard,” The Lancet, 376, 9738, 324-325.

 

 

The National Longitudinal Survey of Youth (NLSY) is a Bureau of Labor Statistics longitudinal study that repeatedly surveys approximately 12,000 individuals every two years.  These individuals, who were selected at the beginning of the survey, are followed over time and surveyed on issues such as the individual’s educational and employment experiences.

Ordered probit regressions and the NLSY can be used to estimate the probability of different levels of educational attainment.  The probability of an individual obtaining a high school or college degree can be calculated based on demographic characteristics, such as race and gender, and household characteristics, such as family structure or parental educational attainment levels.

Regression analysis on NLSY data has also been used to estimate the length of time it takes for an individual’s salary to catch up after an employment termination.  This data can be used to determine the appropriate length of damages in wrongful termination cases.

For more information, see http://www.bls.gov/nls.

The Consumer Expenditure Survey (CE) is conducted for the US Census Bureau and the Bureau of Labor Statistics. The CE is important because it is the only Federal survey to provide information on the complete range of consumers’ expenditures and incomes, as well as the characteristics of those consumers.  It studies the expenditures, income, and household characteristics of American consumers.  The CE is often used in wrongful death cases to estimate a personal consumption factor.

The personal consumption factor is the amount of income the decedent would have spent on personal expenditures as opposed to income going to the household or other members of the household.  Personal consumption includes expenditures on food, clothing, alcohol, transportation, etc.  This factor is generally estimated using the expenditure data from CE and regression analysis.

2012 statsitcs about American spending

For more information, see http://www.bls.gov/cex.

Image Source: http://www.creditloan.com/media/uploads/sites/2/2014/12/paycheck-of-the-average-american-2013.png

Can Microlevel BLS data be used to study how and why employees are paid differently at US employers ?  This paper and the work ultimately looks to provide a method to use the Microlevel, i.e. individual level survey observations, to match dispersion measures like, the standard deviation, in big data BLS employment data. The first step for the researchers is to try and match the aggregate numbers to the micro numbers. 

In this post, we look at the weekly overtime (OT) hours typically worked by registered nurses who work in hospitals. Many of the employees that work in these jobs are not exempt from FLSA overtime pay and earn 1.5 times pay for hours worked over 40 in a given week. The tabulations below are based on U.S. Bureau of Labor Statistics (BLS) survey data. The BLS job title groups are insightful, generally containing more specific job titles with similar knowledge, skills, and abilities (KSA), but can be more broad than a particular company’s job title listing. Also, some companies may have the job title listed here as exempt from FLSA or state OT due to their specific job assignments. The BLS does not make a distinction as to if the job title is exempt or non-exempt from OT.

Occupational Group Title Percent of OT Workers Average Hours of OT 1 out of every 4 (25%) OT workers works at least:
Registered Nurses in Hospitals 15.44% 10.29 hours 52 hours

U.S. BLS data indicates that approximately 15.44% of registered nurses in hospitals work overtime hours in a given week.  On average, these workers that have FLSA overtime work approximately 10.29 hours a week in OT. The average regular or straight time pay rate of these workers in the U.S. is approximately $26.15 an hour.  The average FLSA OT rate, not including supplemental pay such as non-discretionary bonus pay, is $39.22 an hour.

Source: BLS (CPS March)

In this post, we look at the weekly overtime (OT) hours typically worked by those who work as police officers. Many of the employees that work in these jobs are not exempt from FLSA overtime pay and earn 1.5 times pay for hours worked over 40 in a given week. The tabulations below are based on U.S. Bureau of Labor Statistics (BLS) survey data. The BLS job title groups are insightful, generally containing more specific job titles with similar knowledge, skills, and abilities (KSA), but can be more broad than a particular company’s job title listing. Also, some companies may have the job title listed here as exempt from FLSA or state OT due to their specific job assignments. The BLS does not make a distinction as to if the job title is exempt or non-exempt from OT.

Occupational Group Title Percent of OT Workers Average Hours of OT 1 out of every 4 (25%) OT workers works at least:
Police Officers 30.86% 12.6 hours 60 hours

U.S. BLS data indicates that approximately 30.86% of police officers work overtime hours in a given week.  On average, these workers that have FLSA overtime work approximately 12.6 hours a week in OT. The average regular or straight time pay rate of these workers in the U.S. is approximately $27.24 an hour.  The average FLSA OT rate, not including supplemental pay such as non-discretionary bonus pay, is $40.86 an hour.

Source: BLS (CPS March)