Christian received her B.B.A. in Banking and Financial Institutions from Sam Houston State University in August 2022. She began working as an intern this past Spring during her final semester. She enjoyed both her Econometrics for Business course and Intro to Python for Data Science course while also being a member of the International Economic Honors Society, Omicron Delta Epsilon.
Christian’s Favorites Include:
Hobbies:
Running
Playing the Diablo video games
Cooking
Types of Movies and Books:
MasterChef
True Crime
Favorite Foods:
Tex Mex
Seafood
Favorite Quote:
“An investment in knowledge pays the best interest.” – Benjamin Franklin
We are extremely thrilled to have Christian on our team. We offer our warmest welcome to our newest team member.
Ruth received her Bachelor of Arts in Business Administration and Data Analytics from the University of Baltimore in June 2022. She began working as an intern this past Spring during her final semester. While in college she especially liked taking classes in her elective interests such as real estate and discrete mathematics. She also enjoyed her participation in the Astrobees club, where she competed in a NASA SUITS competition.
Ruth’s Favorites Include:
Hobbies:
Reading
Playing Pool
Cooking
Types of Movies and Books:
Doctor Who
Survival and Sci Fi Shows
Favorite Foods:
Shrimp and Grits
Salmon
Favorite Quote:
“Our deepest fear is not that we are inadequate. Our deepest fear is that we are powerful beyond measure. It is our Light, not our Darkness, that most frightens us. We ask ourselves, who am I to be brilliant, gorgeous, talented, fabulous? Actually, who are you not to be? There is nothing enlightening about shrinking so that other people won’t feel unsure around you. We were born to shine, As children do, As we let our own Light shine, We consciously give other people permission to do the same. As we are liberated from our own fear, our presence automatically liberates others.”
We are extremely thrilled to have Ruth on our team. We offer our warmest welcome to our newest team member.
In employment and economic damage cases, knowing the plaintiff’s re-employment opportunities and job search efforts is crucial in calculating damages.
Each plaintiff’s knowledge and responsibilities are used to analyze labor market conditions and supplies. What makes EmployStats unique is our ability to customize and personalize labor market data to best match our plaintiff’s expertise level. We utilize a number of data sources including electronic job search data and public labor data sources such as the U.S. Bureau of labor statistics (BLS) for analyzing labor mitigation cases.
Obtaining this information based on labor market conditions and labor market supply allows us to customize our data based on each plaintiff’s characteristics making the labor mitigation analysis unique.
Performing labor market and mitigation studies requires gathering and using specific information. Often, this information pertains to the plaintiff’s job search efforts within the labor market. For example, did the individual apply for jobs that matched their expertise and education level. Additionally, if an application is made to a different job, we must determine if the job qualifications are similar to their previous position.
Labor market data sources such as, U.S. Bureau of Labor Statistics labor market survey (BLS) and U.S Department of Labor’s ONET, are often used to analyze an individual’s potential job matches. It is through this type of research that an accurate picture of the plaintiff’s job search efforts can be measured and provide needed information in these types of labor market and mitigation studies.
For more information visit Employstats or contact us at info@employstats.com.
Eddie received both his Bachelor of Arts in Economics and Bachelor of Science in Computer Science at The University of Texas at Austin in 2018. He also received his Master in Science in Statistics at Baylor University in 2021. Eddie enjoyed his second course in his programming sequence, Data Structures along with a Game Theory course. Outside the classroom, Eddie participated in the UT quidditch team and got to play in the collegiate championship game his senior year.
Eddie’s favorites include:
Hobbies:
Playing the Guitar
Sports
Chess
Types of Movies and Books:
Drama or Documentary Films
Fiction and nonfiction books
Favorite Books:
The Alchemist
I Know This Much Is True
Favorite Food:
Thai Food
Favorite Quote:
“It does not do to dwell on dreams and forget to live.” – Albus Dumbledore
We are extremely thrilled to have Eddie on our team. We offer our warmest welcome to our newest team member.
EmployStats was brought on to provide our feedback on the best uses of this EEOC-2 data. In these panel meetings, we testified about our industry level experience in using available pay data to analyze claims of disparate pay and employment discrimination. We described to the EEOC how companies like EmployStats, research institutions, and public users utilize federally maintained datasets in practice, comparing the survey data the EEOC collected to other federal databases like the Bureau of Labor Statistics (BLS).
We explained the benefits of current benchmark pay data from different public and private sources, and what additional value the EEO-2 survey data could bring. We also provided EEOC recommendations on best practices for the formatting and publication of the EEOC’s data, so this survey data can be of maximum utility to researchers and the general public.
A few years ago the EEOC had created an additional component to their Equal Employment Opportunity (EEO) survey sent out to employers in the United States, known as Component 2 (EEOC-2 / EEO-2). This addition to their survey asked employers about the compensation of employees and their hours worked, organized by job category, gender, race, ethnicity, and certain pay bands. After collecting this data, the EEOC was interested in analyzing this data and determining how it could be best utilized by both the commission, and the public at large. Partnering with the National Academy of Sciences (NAS), the EEOC formed a panel to closely examine this compensation data, and collect input on its utilization. EmployStats was able to collaborate with several well known professionals including William Rogers, Elizabeth Hirsh, Jenifer Park, and Claudia Goldin.
To discuss a potential case or to answer any questions, you can email info@employstats.com or contact us at 1-866-629-0011.
Calculating unpaid wages, penalties, and other potential wage and hour violations can be a costly endeavor. In some cases, many hours could be spent just getting the payroll and time data into a format that could be analyzed. There are a few things that can help lower time and cost of performing a wage and hour data analysis.
Understand the Time and Payroll Data Before You Start
Getting the employer to provide the background information for the underlying time and payroll data will help save time and money in the future.
Knowing what the different payroll codes mean, how the time and payroll records fit together, the employer’s pay period end and start dates, and the types of bonuses that the employees earned can help streamline the data management and analysis process.
Computer Program Every Step of the Analysis
Use computer programs such as R, STATA, VBA or something similar to handle the data management and analysis. Writing computer code in these types of programs will make managing the time and payroll data and making adjustments to the analysis both easier and replicable.
Establish Clear Objectives
Before you get started, be clear which types of wage and hour violations you need to study. This is particularly important when dealing with time punch data. A lot of time and energy can be saved on the front end by structuring the time and payroll data into a format that will make determining things, such as the hours worked, easier on the backend.
Context of Wage and Hour Analysis
After you establish the types of wage and hour violations, determine the type of violations–local, state or federal. Each type of violation may require different types of analysis.
In the most recent Fisher Phillips Wage and Hour Wednesdays, the Department of Labor (DOL) 80/20 tip ruling allowing employers to take a tip credit for “tip-producing work” was reviewed. Hosted by Fisher Phillips Ted Boehm and Susan Maupin Boone.
TheFair Labor Standards Act(FLSA) allows employers to pay certain employees a direct cash wage below the $7.25/hr federal minimum wage. Employers are allowed to take a tip credit of up to $5.12/hr to make up the difference. However, if the employee’s total wage plus tips does not equal the minimum wage, the employer must pay the difference. If tipped employees spend more than 20% of their working hours in a week performing “directly supporting work”, the tip credit is lost for the excess time and full minimum wage must be paid for that portion of work.
EmployStats Economic Consultant, Matt Rigling, has discovered in his work that differentiating tip-producing work and directly supporting work is important to applying the 80/20 rule. Tip-producing work can be defined as performing tasks such as taking orders, serving, and fulfilling customer needs during meal service and operating hours. Directly supporting work is identified as pre and post service work such as prepping, cleaning, and stocking inventory.
Principal Economists Dwight Steward discusses the economic impact of Tesla beginning production in Austin, Texas, with KEYE-TV Journalist Jessica Taylor.
With new production beginning in January 2022, Dr. Steward believes more jobs and infrastructure will follow. He agrees with expert projections of 5-15,000 jobs being created because the onset of production will result in more jobs and infrastructure. Likely, it will be closer to 15,000 because factory jobs have the potential to create a lot more opportunities according to Steward. Unlike their competitors, Tesla has had no labor and chip shortages.
Elon Musk announced near the end of 2021 that the headquarters will be moving to Austin alongside the gigafactory which began construction during the Summer of 2020.
