The number of cases filed in federal court alleging violations of the Fair Labor Standards Act (FLSA) increased in 2014 for the 10th time in the past 11 years. Moreover, the number of wage and hour lawsuits filed in state courts has either mirrored or exceeded this growth. The increase in wage and hour cases shows no sign of abating.
Furthermore, in March 2014, President Obama directed the Department of Labor to “modernize and streamline” the regulations that determine whether white collar employees are eligible for overtime pay. If the FLSA exemption standards are narrowed, then millions of additional workers may have putative claims of wage and hour violations. Given the steady growth in these cases and the possible change in standards, if an employer has not yet been sued for wage and hour violations, then it is likely only a matter of time until that happens. Additionally, lawsuits filed under the FLSA generally include multiple types of allegations, such as payments below the minimum wage, miscalculation of overtime and unpaid overtime hours (i.e., working “off the clock”).
For wage and hour cases that involve allegations of the miscalculation of the regular rate of pay, the uses of data analysis are obvious. While the parties may disagree about factual issues, such as whether employee bonuses are discretionary (thus affecting whether they should be included in the regular rate of pay), the mathematics behind the proper calculation of overtime pay are seldom in dispute. Similarly, in situations in which workers are paid a fixed salary regardless of the number of weekly work hours, minimum wage violations and the associated economic damages are typically straightforward.
On the other hand, many wage and hour cases include allegations that seemingly cannot be tested with the available data. Intuitively, since time spent off the clock is not tracked in the same way as time “on the clock,” one might fear that off the clock time cannot be measured. However, in the current “big data” era, other data sources may be leveraged to gauge off the clock time. Consider a typical lawsuit involving off the clock work: a group of call center workers allege that they were required to report to work prior to their scheduled start time to boot up their computers and ready themselves to begin taking calls exactly when their shift began. The employees were not paid for any of this pre-shift time, as they were instructed not to punch-in to the time clock until their shift’s scheduled start time.
If these employees’ workplace is secured through an electronic lock, then the exact time that the employees used their electronic key to enter the building is recorded. The amount of time between when employees first enter the workplace and when their paid shift begins represents the maximum amount of off the clock time. Further, if the alleged pre-shift work necessarily involves booting up or logging into computers, then those time-stamps may also be electronically maintained, thus allowing for an even more precise calculation of the pre-shift work time.
Similarly, many wage and hour cases (especially those filed under California state law) center on requirements to provide meal and rest periods. Workers covered by those requirements include non-exempt delivery drivers, who are necessarily performing their duties remotely. A typical lawsuit would allege that the drivers are over-scheduled with deliveries, which in turn prevents them from taking a meal break. Without time data or direct supervision of the employees, this type of claim could be difficult to quantify. However, unconventional data sources can again provide insight into the validity of the allegations.
Modern delivery trucks, for instance, are often equipped with GPS tracking, so the business can provide real-time tracking information to customers waiting for the packages. The GPS data typically include the precise coordinates of the vehicle (i.e., latitude and longitude) and the vehicle’s speed or the amount of time it has not moved. Analyzing these data may show that the drivers’ trucks were almost always in motion, supporting plaintiffs’ contention that there were rarely opportunities for uninterrupted, work-free meal periods. On the other hand, a review of the GPS coordinates of the delivery trucks may suggest that the drivers routinely stopped for potential meal periods. Overlaying the GPS coordinates when the driver stopped with satellite imagery may indicate that periods of inactivity occurred when the delivery truck was in a McDonald’s parking lot.
Electronic data can also help in analyzing questions relating to conditional certification or decertification of a class. The amount of pre-shift work, as measured by the time between when employees log into their computer and when their paid shift begins, may differ from one location to another owing to operational differences. Alternatively, this amount may change over time among employees at the same location, owing to management turnover, technology improvements, etc. To the extent that there are underlying patterns in the alleged wage and hour violation, the electronic data can be “mined” to find them. Identifying these patterns can lead to a more narrowly tailored putative class. In cases where the factors that affect potential pre-shift work are numerous (e.g., location, supervisor, job, seasonality), an employee’s individual circumstances begin to predominate over the common claims in the lawsuit, which could arguably lead to decertification.
The availability of electronic data does not automatically mean that a common method of proof exists for establishing liability or assessing damages. For example, in cases with allegations of missed meal breaks, the data can only show when employees received a compliant meal period. If the employee did not have a meal break, the data cannot indicate if a meal period wasn’t provided or if the employee opted to skip the break to finish his or her shift early. Similarly, using electronic data for a representative group of plaintiffs to compute economic exposure may be mathematically sound for determining aggregate damages, but applying an average liability calculation to every member of a putative class could result in sizable windfalls for some and significant shortfalls for others. Whether this type of outcome is sufficient to warrant class treatment is obviously a question for the fact-finder, but an analysis of the electronic data may prove to be persuasive.
Most of the thousands of wage and hour cases filed each year never proceed to trial. The vast majority of these cases are resolved through a settlement that is based on an economic valuation of the alleged violations. Obtaining an accurate assessment of the potential liability in wage and hour cases is critical in successfully resolving these disputes and is often beneficial in preparing the optimal strategies relating to class certification.