Wage and hour cases

Making Wage and Hour Data Analysis Cost Effective

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.

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• 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.

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• 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.  

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• 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.

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• 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.

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. 

For more information visit Fisher Phillips.

David Neumark of the University of California, Irvine, and EmployStats academic affiliate, has been featured in the finance and economics section of The Economist’s 2020 edition.  The article focuses on the topic of wage floors and the cause and effect of increasing minimum wage requirements. A minimum wage policy raises wages for workers, but the money required to support higher minimum wages has a potential to hit poorer bosses’ harder.

Professor Neumark’s paper, co-authored by Lev Drucker and Katya Mazirov, referenced in The Economist article, examines the potential effect increasing the minimum wage may have on businesses.  The author’s paper titled “Who Pays for and Who Benefits from Minimum Wage Increases? Evidence from Israeli Tax Data on Business Owners and Workers” offers insight into potential unintended consequences of increased wage floors.

The Economist article can be found here.

Drucker, Mazirov, and Neumark’s paper can be found here.

Unpaid travel time claims are prevalent in wage and hour litigation matters.  Attorney’s typically will turn to economic consultants to calculate the travel distance and travel time in such cases. Unpaid travel time can result in unpaid straight time pay and unpaid overtime premium pay if the addition of travel time to time worked exceeds the overtime threshold. It is important for attorneys to understand the process consultants use in order to effectively and knowledgeably communicate the analysis their client needs.

What we use:

We utilize Google Maps API to calculate travel time and travel distance.  In order to calculate travel, it is important to first organize the data to most efficiently calculate travel.  Google allows 2,000 origin to destination requests per day per user. Travel only needs to be calculated for unique destinations that can then be applied to the data and potentially duplicate to and from destinations. Google Maps API can calculate the expected travel time between two locations at a specific time of day or the average amount of time taken to travel that distance. This can be useful if traffic plays a significant role between locations and if workers are located in heavily trafficked areas.

How we use it:

The types of travel performed by non-exempt workers vary case by case. In some instances, the travel time between the employee’s normal work location and their job site location may be compensable.  For example, an employee may need to drive from their home to their work location or employers office before driving to their job site location to begin their day. We have calculated travel time in this manor for technicians, painters and pest control, and construction workers. These are just a few examples of workers that may experience this type of unpaid travel.

Additionally, we have calculated travel time in cases of home health caregivers. These home caregivers were only paid for time spent with patients and were not paid for time spent traveling between patients homes. After organizing the data, we were able to determine the order of patients each caregiver visited and calculate the travel time between their first patient and their second patient of the day, and so on. This type of travel time calculation may be helpful in travel time matters where employees travel to and from different locations throughout their work day. 

The frequency of trips is dependent on the types of employees and the circumstances of the case. One common factor that is required in all wage and hour matters involving travel time is complete and detailed data. 

Complex wage and hour litigation often involves significant data management and sophisticated analyses in order to assess potential liability and damages. This article highlights common wage and hour data management issues, sampling and surveying, as well as provides a case study as an example of the use of sampling in an overtime misclassification case.

Download Dr. Dwight Steward and Matt Rigling’s paper on wage and hour expert economists here!

Economics and Statistics Experts in Wage and Hour Litigation

The scope of Wage and Hour cases can extend beyond traditional claims on overtime or off-the-clock work. The same analytical principles can extend, for example, to cases involving employee reimbursements. EmployStats has recently worked on a case in California where the Plaintiffs allege they were not reimbursed for routine miles traveled in personal vehicles between job sites, despite the Defendant’s stated policy.

The EmployStats team assessed the Plantiffs’ theory of liability and estimated unreimbursed expenses based off of the available case data on mileage, parking, and toll charges. The analysis presented to the court showed a significant difference between stated and actual reimbursements for miles traveled by the Plantiffs. Based off of the analysis and other evidence at trial, the court certified the Plaintiff class.

The EmployStats Wage and Hour Consulting team’s trial plan is as follows:

1. First, the EmployStats team would survey a statistically representative sample of class members about the existence of unreimbursed miles, using a random sampling methodology to eliminate potential bias.
2. Next, the team would use a similar statistical sampling methodology to determine the typical miles traveled by the class members, and combining this resulting data with mapping platforms (ex. Google Maps API) to calculate distances in miles traveled between job locations.
3. Finally, Employstats would tabulate damages based off of these results, using publicly available data on reimbursement rates for miles traveled in personal vehicles.

A copy of the court’s order can be found though the link here: McLeod v Bank of America Court Order – Dwight Steward PhD Statistical Sampling Plan

To see how EmployStats can assist you with similar employment or statistics cases, please visit www.EmployStats.com or give us a call at 512-476-3711.  Follow our blog and find us on social media! @employstatsnews

EmployStats is honored to be attending and speaking at the upcoming National Employment Lawyers Association (NELA) Spring Seminar.  The seminar, titled Epic Advocacy: Protecting Wages in Litigation & Arbitration will take place in Denver, CO on April 12-13, 2019.  

 

EmployStats’ principal economist Dwight Steward, Ph.D., and Matt Rigling, MA, will be presenting alongside attorneys Michael A. Hodgson and Dan Getman.  The speaker’s session, Calculating Damages: Views from an Expert and Lawyers, will discuss all relevant aspects of calculating and proving liability and damages in wage and hour cases.

 

The panelists will present the options attorneys face when attempting to tabulate damages, discuss the best practices for obtaining and analyzing data, as well as discuss common wage and hour issues such as sampling and surveys.  EmployStats’ statistical experts will also provide statistical background as they relate to labor and employment class action lawsuits, such as a explaining statistical significance, confidence intervals, stratified sampling, and margin of error.

 

We hope to see you at the upcoming NELA Spring Seminar in Denver on April 13, we would love to meet and discuss how EmployStats can assist you with your wage and hour lawsuit.  To find out more about the seminar, please visit the NELA Website. For more on EmployStats, visit the EmployStats Website.

A common allegation in wage and hour lawsuits is off-the-clock work.  In these types of cases, employees usually allege that they performed work, such as travel between job sites, that they were not paid for performing.  Other common off-the-clock-work allegations typically involve activities such as spending time in security checkpoints, putting on a uniform, preparing for work, and logging onto computer systems.

 

Recently, the EmployStats Wage and Hour Consulting team completed work on a case where Plaintiffs alleged unpaid off-the-clock work for time spent driving from their homes to their job sites, as well as travel time between job sites.  In this case, EmployStats was able to analyze and assess Plaintiffs’ allegations by combining and creating datasets of personnel and job location data, and using mapping programs to calculate the time Plaintiffs could have potentially spent traveling and performing off-the-clock work.

 

The following is an example of how the EmployStats Wage and Hour Consulting team typically handles a case involving travel time:

1. First, the Employstats team works to combine and merge multiple databases containing employee home locations, employee time and payroll records, and job site locations into a single analyzable database.
2. The EmployStats team then uses mapping platforms, such as Google Maps API or Mapquest API, to calculate the distance in miles and/or travel time in hours for each unique trip.
3. Finally, the EmployStats team uses the employee time and payroll records to assess any potential damages due to travel time off-the-clock work.

 

Check out the EmployStats website to see how we can help you with your wage and hour cases!

The EmployStats Wage & Hour Consulting Team recently completed work on a case in the state of New York where the Plaintiff’s alleged unpaid straight time and overtime compensation due to the Defendant’s timekeeping policies.

In this case as well as others that EmployStats has worked in the past, the Plaintiff’s alleged that the Defendant’s had a timekeeping policy which systematically understated the employee’s time worked in a given pay period.  In practice, some time clock rounding policies may be neutral in principle, but non-neutral in practice. For any number of reasons, the employee or the employer may benefit more often than not from a seemingly neutral rounding policy.

The analysis that we perform typically involves manipulating, matching and analyzing big data from inherently incompatible time and payroll databases.  In addition to analyzing the alleged straight time and overtime compensation owed to employees, EmployStats also assists attorneys in the calculation of penalties.

In states such as California and New York, there are penalties for noncompliance with the labor codes.  We work with attorneys to calculate the appropriate penalties and interest in the lawsuit or investigation.  The EmployStats Wage & Hour Consulting Team is proficient at providing calculations and tabulations that are insightful and well documented.

 

The EmployStats consulting team, lead by Matt Rigling, MA, recently worked on a case involving employee misclassification.  EmployStats assisted attorneys by calculating potential damages for employees who were classified as exempt but potentially should have been classified as non-exempt and therefore owed FLSA overtime wages for hours worked over 40 in a workweek.

 

Matt Rigling and the EmployStats team also worked to use the case data and information provided to confirm whether the employees in question passed both the salary and duties tests for exemption purposes. According to the FLSA, an employee can be classified as exempt under the Administrative, Executive, or Professional exemption if they meet all of the requirements for salary and job duties.  

 

In this case, EmployStats compared the employee information to the salary and job duty requirements of the Administrative and Executive exemptions.  Under both exemptions, the employee must be paid a salary of at least $455, as well as meet the job duties specific to an Administrative or Executive employee.  According to U.S. Department of Labor, Administrative employee’s primary job duty must be office work that is “directly related to the management or general business operations of the employer or employer’s customers” and must include “the exercise of discretion and independent judgement” for matters of importance.  Similarly, Executive employee’s primary job duty has to be managing the company, or a department of the company. Additionally, they must also regularly direct at least two other full-time employees and have the authority to at least recommend the company fire, hire, or promote other employees.

To see how EmployStats can assist you with an employee misclassification case or another labor and employment matter, please visit www.EmployStats.com or give us a call at 512-476-3711.  Also make sure to follow our blog and find us on social media! @employstatsnews