Tag: wage and hour

Employment and Wage & Hour Statistics Focus: Consumer Price Index

The Consumer Price Index (CPI) is monthly data released by the Bureau of Labor Statistics on the change in prices paid by urban consumers for a representative basket of goods and services. The CPI is available by region and consumer type. It is most often used to measure inflation, which is an important concern when present-valuing economic damages in the future. Future damages must be discounted by the rate of inflation, because one dollar today is worth more than one dollar tomorrow.

Note: Even though CPIs differ by city, it is not appropriate to use CPI data to compare the cost of living between cities. The CPI does not measure price differentials between cities, but rather only over time. The representative basket of goods and services varies with geographic location.

For information on the Consumer Price Index, please refer to www.bls.gov/cpi

Image source: http://theregister.co.nz/news/2015/08/new-zealands-consumer-price-index-it-accurate-enough

Employment and Wage & Hour Statistics Focus: Census

Conducted every ten years, the Census is the largest survey in the United States. The 2010 Census represented the most massive participation movement ever witnessed in the US, with approximately 74% of households returning their census by mail. The Census Bureau hired about 635,000 employees to walk through neighborhoods throughout the United States to count the remaining households.

The 100% characteristics form was used with every person and housing unit in the United States. It includes information on sex, age, and race by geographic location. Census data is available at many geographic levels, including blocks, zip codes, county, and state.

The 2010 Census asked detailed questions that include information on educational attainment, marital status, labor-force status, and income. The Census is a very large database and hence has many uses ranging from racial profiling in police-stop baselines to wage data.

For more information, please go to www.census.gov/

2015 FLSA OT report for Food Service Managers

In this post, we look at the weekly overtime (OT) hours typically worked by food service managers. 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:
Food Service Managers 47.14% 16.59 hours 60 hours

U.S. BLS data indicates that approximately 47.14% of food service managers work overtime hours in a given week.  On average, these workers that have FLSA overtime work approximately 16.59 hours a week in OT. The average regular or straight time pay rate of these workers in the U.S. is approximately $18.57 an hour.  The average FLSA OT rate, not including supplemental pay such as non-discretionary bonus pay, is $27.85 an hour.

Source: BLS (CPS March)

FLSA OT report for Telemarketers

In this post, we look at the weekly overtime (OT) hours typically worked by telemarketers. 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:
Telemarketers 21.43% 11.67 hours 60 hours

U.S. BLS data indicates that approximately 21.43% of telemarketers work overtime hours in a given week.  On average, these workers that have FLSA overtime work approximately 11.67 hours a week in OT. The average regular or straight time pay rate of these workers in the U.S. is approximately $8.48 an hour.  The average FLSA OT rate, not including supplemental pay such as non-discretionary bonus pay, is $12.72 an hour.

Source: BLS (CPS March)

FLSA OT report for RN’s working in hospitals

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)

FLSA OT report for police officers

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)

FLSA OT report for individuals working in truck driving occupations

In this post, we look at the weekly overtime (OT) hours typically worked by those who work in truck driving occupations.

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:
Driver/Sales Workers and Truck Drivers 44.11% 14.5 hours 60 hours

U.S. BLS data indicates that approximately 44.11% of truck drivers work overtime hours in a given week.  On average, these workers that have FLSA overtime work approximately 14.5 hours a week in OT. The average regular or straight time pay rate of these workers in the U.S. is approximately $18.74 an hour.  The average FLSA OT rate, not including supplemental pay such as non-discretionary bonus pay, is $28.11 an hour.

Source: BLS (CPS March)

Big data question: How big of a random sample is big enough in a wage in hour case?

That’s a question that comes up a lot in wage and hour land employment lawsuits.  Typically the question is how many employees do I need to  look at to have a statistically significant sample?

bigdataIn some instances it’s not feasible to collect data or get all the records for
all the employees of a particular company. Sometimes the data is kept
in such a way that it takes a lot of effort to get that information.  In
other instances it is a matter of the limitations of imposed by the court.

In any event, that’s a question that comes up a number times in wage and hour lawsuits particularly ones involving class or collective actions. So what’s the answer?

Generally, the size of the sample needs to be sufficiently large so that it is representative of
the entire employee population. That number could be relatively small say 40 employees or relatively large say to 200 employees depending on the number of employees at the company and the characteristics of the employee universe that is being analyzed.

For example if there are no meaningful distinctions between the employees in the universe, that is
it is generally accepted that all the employees are pretty much all
similarly situated, then a sheer simple random sample could be
appropriate.

That is, you could simply draw names from a hat, essentially. A simple random sample typically requires the smallest number of employees.

If there are distinctions between employees that need to be accounted for, then
either a larger sample or some type of stratified sampling could be appropriate.
Even if there are distinctions between employees, if the sample is sufficiently large then distinctions between the employees in the data could take care of themselves.

For instance, assume that you have a population of 10,000 employees and they are
divided into four different groups  that need to be looked at differently.

One way to do a sample in this setting is to sample over each of the different groups of employees separately. The main purpose of the individual samples is to make sure that you have the appropriate number of employees in each of the different groups. That is, to make sure that the number of employees in the different samples are sufficiently representative of the distribution of the different groups of employees in the overall population.

Another way to do this is to simply just take a large enough sample so that the distinctions take care of themselves.  If the sample is sufficiently large then the distribution of the different groups of employees in the sample should on be representative of the employee population as a whole.

So in this example, if there is a sufficiently large sample it could be okay to use a simple random sample and you would get to the same point as a more advanced stratified type of approach.

The key however is to make sure that the sample is sufficiently large that of course depends on the overall population and the number of groups of employees being studied.

What’s a pension band?

A pension band is a figure that is used when calculating the monthly benefit for a defined benefit pension program.  A pension band figure, which is typically changes each year, is generally a dollar amount that the person will receive monthly for each year of service with the employer when they retire..  The pension band generally varies by job title/grade level/occupation covered by a pension plan.. Once assigned, the job title, grade level and occupation will generally remain in that band unless the job title, grade level and occupation (by location) are later reclassified to a different pension band.

Example of Basic Monthly Benefit Calculation.

The following hypothetical example shows how a basic monthly benefit is calculated assuming: •

You are in pension band 115.

The monthly benefit for pension band 115 is $55.49

In this example, the monthly benefit for a person with 30 years of credited service would be

Monthly benefit for pension band 115 ($ 55.49 Multiplied by 30 years of net credited service x 30 Basic monthly benefit =  $1,664.70

Comparsion of CPS matched data sets – Millmet et al (2002) to Steward and Gaylor (2015)

Big Data. Bureau of Labor Statistics. Survey data. Employment Big Data.  Those are all things that calculating worklife expectancy for U.S. workers requires.  Worklife expectancy is similar to life expectancy and indicates how long a person can be expected to be active in the workforce over their working life.  The worklife expectancy figure takes into account the anticipated to time out of the market due to unemployment, voluntary leaves, attrition, etc.

Overall the goal of our recent work is to update the Millimet et al (2002) worklife expectancy paper and account for more recent CPS data. Their paper uses data from  the 1992 to 2000 time period. Our goal is to update that paper using data from 2000 to 2013. The main goal of the paper is to see if estimating the Millimet et al (2002) econometric worklife models with more recent data changes the results in the 2002 paper in any substantive way.

 

Our approach is two fold.  First we matched the BLS data cohorts based on the Millimet et al. (2002) and Peracchi and Welch (1995) papers. In a nutshell the CPS matching routine involves matching incoming and outgoing cohorts across a given year.  Once the data is matched, we then look at the work status of the individuals to determine if they were active or in active across the year that they were interviewed by the BLS. . We were able to create a match CPS data set of 201,797 individuals where as the Millimet et al. (2002) found 200,916 matched individuals.

Table 1. Comparsion of CPS cohort matched data sets
Year Millimet et al.  (2002) Steward and Gaylor (2015)
1992/93 37,709 36,652
1994/95 34,418 33,377
1996/97 31,691 32,739
1997/98 32,276 32,972
1998/99 32,083 32,893
1999/2000 32,739 33,164
Total 200,916 201,797

Notes:

The CPS data was matched using the algorithm similar to Millimet et al (2002) and Peracchi and Welch (1995).  Households in rotation 1-4 were matched using the household identifier number to the same household in rotations 5-8 of the following year. Individuals had to have the same sex, race and be a year older in rotation 5-8 to be determined a match.