The U.S. Department of Labor’s Wage & Hour Division recently announced its proposal to amend 29 C.F.R. Part 541, containing the “white collar” exemption for executive, administrative, and professional employees. The Division’s proposal would dramatically increase the salary levels required for the exemption and the highly-compensated exemption. The standard salary is projected to be $970 per week or $50,440 per year, and the highly-compensated employee standard salary would be set at $122,148 starting in 2016. If the proposals become law, the requisite salary levels for these exemptions are scheduled to increase automatically each year.
Given the size of the increase to the minimum required salary level, many employers may be forced to reclassify portions of their workforces to nonexempt status. It is crucial that employers take steps to plan for the impact that reclassification could have on their budgets. As a threshold matter, employers must determine who in their businesses will be impacted by the proposed salary increase. In other words, employers must know who among their employees are currently classified as exempt but earn less than the proposed salary amount of $50,440.
Once an employer identifies the employees for whom this change will impact, it will need to consider how reclassifying some or all of them would impact the company’s bottom line. Not surprisingly, most businesses do not track the hours worked by their exempt employees. As a result, it can be difficult to determine how many “overtime” hours these impacted employees will be likely to work if they are reclassified to nonexempt status. This is where proxy data and pilot studies can help employers plan for changes in wage-related expenditures.
Proxy data are preserved characteristics of an environment that can stand in for direct measurements. For example, insurance companies have realized that students with good grades are often better drivers, and therefore use G.P.A. as a “proxy” to measure safe driving behavior. Businesses often create and preserve proxy data that can help establish the approximate number of hours worked by exempt employees who do not currently track their time using traditional methods (e.g., time clocks or time entry). These types of proxy data include:
- Key or badge swipe records;
- Alarm login and logout records;
- Records reflecting times when employees logged in or out of a computer network;
- Cash register login and logout records;
- Video surveillance footage;
- Email records; and
- GPS data (especially for driving-related occupations).
Employers can use these types of data to estimate the number of hours an exempt employee works in a given week.
In addition to extrapolating the number of hours worked from proxy data, businesses can use pilot studies to estimate how many hours exempt employees work per week. Employers using pilot studies must consider how to select a representative sample of employees and how to accurately track the employees’ time. Although pilot studies may offer a more accurate portrayal of an employee’s work hours, companies using these studies will also need to train participating employees on proper timekeeping methods.
Proxy data and pilot studies can help employers plan for budgeting by allowing them to more accurately anticipate the expenditures associated with paying overtime payments to employees who become non-exempt. Employers could also use the data to set reclassified employees’ wage rates (including the anticipated overtime pay) at a level where the expected weekly pay does not change following the reclassification.
For example, imagine that John Smith works for Company A as an exempt employee and his weekly salary is $660. Company A has determined that it will need to reclassify John as a nonexempt employee due to the DOL’s new proposed overtime rules. After examining the times that John logged into and out of the company’s computer network, Company A estimates that John works 50 hours per week on average. If Company A simply converts John’s salary to an hourly rate without consideration of his typical overtime hours, his wages would be set at $16.50 per hour ($660/40 hours per week). Armed with the knowledge of John’s typical working hours, Company A could instead set John’s hourly rate at $12.00 per hour and his weekly pay would still amount to $660, assuming he continues to work 50 hours per week ($600 regular pay + $60 for the overtime premium). In contrast, if his hourly wage was set to $16.50, then John Smith’s typical weekly earnings would increase to $907.50
While proxy data and pilot studies can help employers plan for the impact of the DOL’s proposed overtime rules, there are some potential obstacles to consider when using these tools. First, employers cannot expect total accuracy in estimations of hours worked because most forms of proxy data are not intended to track actual hours worked. The available data may be incomplete or misleading. For example, proxy data drawn from key, badge swipe, or alarm records may demonstrate the duration that an employee was physically present at the worksite, but may lead to an overestimation the number of hours the employee spent performing compensable work. Similarly, pilot studies performed on less than the total impacted population cannot calculate employees’ work hours with complete accuracy. Moreover, depending on the employer’s technological capabilities and the type of proxy data available, it could be expensive or time consuming to collect, process, and analyze the data needed for the study. Finally, employers using proxy data or pilot studies must gather detailed, weekly data to generate a useful assessment of hours worked. Relying on an average number of hours worked per week can distort the true cost of an employee’s overtime work by masking the fact that a particular employee works a higher number of overtime hours in a small number of weeks. This is particularly true where most of the overtime hours occur seasonally, such as with retail employees during the holidays.
Despite these challenges, many employers will find that using proxy data or pilot studies will be well worth the effort. With thoughtful and creative use of proxy data or pilot studies, employers can more accurately predict, and even minimize, the financial impact of reclassification.