The health care and social assistance industry gained 9,200 jobs from August 2015 to September 2015. Compared to September 2014, the cumulative number of jobs added in this industry is 67,000, an annual increase of 5.0%.
Source: http://www.tracer2.com/admin/uploadedPublications/2138_TLMR-Current_Edition.pdf
Image source: http://blogs.wsj.com/health/2012/01/06/health-care-sector-adds-jobs-as-overall-employment-picture-looks-healthier/
The oil and gas extraction industry in Texas lost 900 jobs from August 2015 to September 2015. Compared to September 2014, the cumulative number of jobs lost in this industry is 1,800, a decrease of 1.7%.
Source: http://www.tracer2.com/admin/uploadedPublications/2138_TLMR-Current_Edition.pdf
Image Source: http://www.eliteexploration.com/texas-oil-gas-companies/
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)
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)
The American Time Use Survey (ATUS) is commonly used to estimate the amount of time spent on household services for valuing losses in personal injury and wrongful death cases. ATUS is sponsored by the U.S. Bureau of Labor Statistics and administered by the Census Bureau. It measures the amount of time people spend doing various activities, such as working for pay, caring for children, volunteering, commuting, and socializing.
Data collection for the ATUS began in January 2003, and estimates are released annually. The most recent survey was conducted in 2014, and approximately 11,600 individuals were interviewed. The participants were asked to account for every hour between 4 a.m. the day before the interview and 4 a.m. the day of the interview. Using this data, it is possible to construct the average number of hours spent on household services based on demographic characteristics. The categories most often used to measure household services are time spent:
The valuation of lost household services is performed by calculating how much it would take, in dollars, for the family to replace the services that were provided by the injured or deceased person. In other words, how much it would cost for the family to go out into the marketplace and pay for each of the individual services, such as having a person mow the lawn or take care of the financial records?
Once the market value of the service, expressed as an hourly rate, is determined, the final replacement cost of each service is calculated by multiplying the hourly market rate by the average number of hours spent performing the services, as reported in the ATUS. For example, in the 2003 ATUS it was reported that the average 24-year-old female spends 2.99 hours per week on food cooking and cleanup. The replacement cost for these services is $9.60 an hour. Therefore, the replacement cost for a week of food cooking and cleanup is $28.70 (2.99 x $9.60) and the annual replacement cost is $1,492.61 (2.99 x $9.60 x 52). The calculation is then performed for all household services for each remaining year of the person’s life expectancy. Finally, the summed loss of household services value must be discounted back to the present day.
Random sampling of time records is often necessary in wage and hour cases. Some employers maintain handwritten or machine produced records that are not efficiently converted to an electronic format. It is not always possible to convert these types of records in a timely and cost effective manner.
Random sampling is a tool that is frequently called upon in these situations. For instance, it is common to randomly sample a population of employees and then randomly sample a time period from that sample of employees. This type of cluster random sampling is common in these situations.
See for example: Sharon Lohr, Sampling: Design and Analysis, 2nd Edition, Brooks/Cole, 2010.
Many employer time records are still in paper format that is not easily machine readable. Analyzing these data in a wage and hour case typically requires manual entry of the records. However, entering in all the time records is not always feasible. In these situations a statistical random sampling of records can be useful and informative. A solid statistical sampling alows the researcher to calculate error rates which are useful when making inferences concerning the time records.