By: Leonore Ralston, CFE, CAMS
(Affiliate Financial and Damages Expert with Financial Evidence Group) and
A. Christine Davis, CPA/CFF/CGMA
(Accounting, Financial and Damages Expert)
“In 2000, a California wage and hour class action was very rare and a representative wage and hour action was unheard of. Things have changed dramatically. Class actions and representative actions are now ubiquitous. Currently, numerous such actions are filed every day in California courts.” (From Seyfarth Shaw LLP’s Litigating California Wage & Hour Class and PAGA Actions 22nd Edition)
Whether a wage and hour case is a top-10 settlement contender or something more modest, the damages expert should measure damages by molding employee data into a robust model which reconciles that data to the applicable law yet is flexible enough to accommodate reasonable assumptions and simple enough for a settlement conference judge or mediator to quickly comprehend. Not only must the model be tailored to the federal or state laws that are alleged to have been violated - it will also be influenced by how the employee data was originally collected, maintained, and produced. That is the topic of this article, which uses assignments we have been involved in as case studies.
Generally speaking, with plaintiffs aiming to recover back pay, the following formula is applied in calculating estimated unpaid wages before interest, penalties, and liquidated damages:
While the formula is simple, arriving at estimated unpaid wages [C] can be quite challenging depending on the state of available data. Furthermore, under California’s Private Attorneys’ General Act, better known as “PAGA,” the value of unpaid wages is dwarfed by the value of related penalties, as most PAGA penalties are imposed per pay period, per employee. The basis for PAGA penalties is not the value of unpaid wages, but rather the number of violations per employee per pay period. The process of estimating PAGA penalties is undoubtedly also impacted by the condition of available time and pay records.
Focusing on estimated unpaid wages [C], the expert independently recalculates wages due under the applicable law [A], quantifies wages actually paid to the subject employees [B], and compares the two values. Estimated unpaid wages [C] is the result of subtracting [B] from [A]. Naturally, if [B] is greater than [A], there are no unpaid wages. The primary data sets the expert relies upon are timekeeping records and payroll records; they are the foundation for quantifying potential damages.
Despite the seemingly straightforward formula above, the underlying work is often complex and not without hurdles. The process of studying, organizing, and analyzing the data that results in the values for [A] and [B] on an individual employee basis for all members of a class or collective action renders the damage analysis not only highly data-intensive, but possibly thorny when unanticipated data-related challenges arise in the process.
In our experience, a number of common data-related challenges emerge when analyzing wage and hour claims due to a variety of factors, such as the unique way the employer has maintained its records, a lack of alignment between work periods and pay periods, or the sheer volume of the data involved. Experience has shown, however, that such hurdles can be overcome by working closely with counsel early in the process and engaging in productive and frequent communication with the client when possible.
While this article discusses examples of such challenges, it also highlights the importance of using the employer’s timekeeping and payroll data correctly by: (1) understanding how the employer collects, maintains and reports the employee data; (2) identifying the relevant data and periods to analyze; and (3) utilizing suitable tools to organize the data so that the expert analysis can commence and estimated unpaid wages, if any, are determined in a cost-efficient manner. The following case studies are illustrative.
Case Study No. 1 — Dealing with Format Issues
In a collective action involving Section 7(k) [1] employees, the plaintiffs allege their employer calculated a regular rate of pay that was not in compliance with the Fair Labor Standards Act (“FLSA”) [2]. The plaintiffs claim that the regular rate should have included a specific type of cash compensation, but it did not, and the omission resulted in the underpayment of overtime wages. The timekeeping data resides in three years’ worth of timecards that were filled in by hand by hundreds of employees. Furthermore, sporadic prior-period corrections made in a current period time sheet need attention, and some entries are illegible. The timecards also display total pay period hours that do not always correspond to the sum of the recorded daily hours. For strategic reasons, the client requests a preliminary damages assessment within 30 days. The time data, however, is not primed for immediate quantitative analysis.
In our illustrative case, manual entry of handwritten timecards is performed to create an Excel file to facilitate the expert analysis. In a similar situation involving time-stamped entries, daily clock-in and clock-out stamped times might be detailed to the minute, but the same source document might also display a handwritten daily total rounded to the hour. Depending on the specific allegations or the scope of the expert’s assignments, both the clock-in and clock-out stamped times and the rounded total hours for the day might have to be considered for supplementary analysis. If the supplementary analysis is added to the expert’s planned work program, the incremental data will also have to be converted into a spreadsheet to facilitate a reconciliation.
When data entry is necessary, not only does the expert carefully perform and supervise the data entry; the expert must also validate or audit the converted data for accuracy and completeness. Where the damage expert might receive poor copies of handwritten timecards, or records wherein certain entries are illegible, the expert should find ways to validate the correct number of hours recorded for the day by corroborating the results of the converted data with available source data. For example, for a given pay period, the expert can compare the sum of the converted total daily hours to the total pay period hours displayed on the timecard and in the payroll data, and then correct and solve any identified discrepancies.
Unlike the existing data format in the example above, the ideal data file format for the damages expert s electronic, such as Microsoft Excel or Access files. The reason is that the quantitative analysis is commonly designed and constructed in Excel. As described above, before the expert can begin the quantitative analysis, the expert must convert the paper formats into an electronic file such as Excel, by either manual data entry or by using a PDF conversion software. Therefore, receiving the source data for timekeeping and payroll information in electronic format creates significant cost efficiencies while eliminating inadvertent data entry or conversion errors. It is common, however, that the timekeeping and/or payroll information come in less desirable configurations, like handwritten timecards, stamped timecards, poorly scanned PDF’s or hard copies of individual pay stubs.
When timekeeping or payroll information is available in PDF files, using a PDF conversion software rather than data entry to create an electronic data file is an immediate option, but not guaranteed. While there are various software programs that can convert PDF files into spreadsheet format, the integrity of the converted data is not a given, and the steps the expert should take to ensure data integrity depend on the quality of the PDF files. The best PDF quality exists when, for example, the employer’s payroll software is capable of exporting the information directly into a PDF format.
Alternatively, an example of problematic PDFs are copies of printed pay stubs that are poorly scanned, or a rescanned PDF, because these can result in data fields not aligning from one page to the next during PDF conversion. In the latter example, the conversion process requires increased attention and time to complete, or, if the expert determines that the initial conversion output is unreliable, the expert will wisely choose to enter the requisite data manually to create an electronic file.
Input from the damages expert early in the project can positively influence how the timekeeping and payroll data are produced. Often, the quality of that data significantly governs the time required to scrub the raw information prior to actual analysis, naturally impacting costs.
Case Study No. 2 — Dealing with Incomplete Data
In a prelitigation project involving over 1,000 employees, the employer proactively seeks to know its potential exposure pertaining to a three-year period. Retained by the employer, the consulting expert has the benefit of communicating freely with the employer’s payroll manager, who is the custodian of the payroll data. A seemingly smooth electronic data transfer to the expert occurs. Upon completing some preliminary review and testing of the data, however, the expert immediately recognizes that certain critical pay codes are unaccounted for. Upon inquiring of the client, it is then discovered that the payroll manager had inadvertently filtered the payroll data provided to the expert, resulting in the omission of the very codes that are the subject of the anticipated dispute.
As the case above illustrates, even when the employee data is already in electronic format, the expert should perform tests for data completeness [3] to confirm that all available timekeeping and payroll information has been received - for all employees at issue and for the entire relevant period.
While incomplete or unclear data might be initially produced for a variety of reasons, the expert may be in a position to course correct. For example, the expert for the employer has the benefit of direct access to the employer’s relevant personnel in human resources, payroll, or information technology, as the expert formulates detailed data requests and raises questions to obtain clarity regarding the data fields in the time and payroll records. When engaged to support the plaintiffs, the expert works with plaintiffs’ counsel to generate useful and timely discovery requests. Because the plaintiffs’ expert is precluded from having direct contact with the employer, it is beneficial to get the expert involved early in the discovery phase where their expertise can be leveraged to help prepare interrogatories, deposition questions or necessary discovery motions.
Case Study No. 3 — Dealing with Large Volumes of Data
In a putative class action involving over 8,000 employees who worked in over 50 locations in California, the employees claim that their employer violated state labor laws by allegedly failing to pay correct overtime wages, failing to provide compliant meal periods and rest breaks, and failing to furnish timely and accurate wage statements, among other things. The relevant period to be analyzed spans almost nine years. The employer’s raw time data in electronic form could easily occupy the equivalent of at least five million rows of data. Excel, however, can only hold a little over one million rows of data.
Depending on the liability period, the size of the class, and the type and number of pay codes involved, the collective size of the time and payroll data might be too large to be maintained and analyzed in otherwise dependable programs like Excel or Access. For example, in each employee pay stub, relevant data might consist of the following for each pay period: (1) a column for each type of information (e.g., employee number, employee name, pay code, hours, wages paid); and (2) a separate row for each pay code with wages or deductions. Here, a single employee pay stub could easily occupy more than a dozen rows. In the case above, the requisite data pertaining to the over 8,000 putative class members is too large to manage and analyze in Excel.
When Excel’s maximum capacity is an issue, the expert utilizes other types of software that can house all of the time and payroll data, such as Stata, CaseWare IDEA or Microsoft SQL Server. Pertinent subsets of the data can then be exported to Excel for targeted expert analysis or sample testing, if such testing in Excel is preferable.
Conclusion
Because every employer has its own distinct process of recording, processing, and maintaining time records and has its own set of pay codes, data-related challenges can be expected in connection with wage and hour damages analyses. Nevertheless, informed by a good understanding of applicable law, the successful damages expert effectively utilizes the available data from every new environment by being adaptable and creative, alert and inquisitive, and self-correcting and prompt. For the expert who is adept at navigating through the challenges, anticipating the next unplanned step is actually part of the plan.
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[1] Section 7(k) of the FLSA (29 U.S.C. § 207(k)) provides public employers the option to implement an exemption from the seven-day, 40-hour overtime threshold for public safety employees (fire protection and law enforcement personnel). The "7(k) exemption" increases the overtime limit and gives the employer the flexibility to select anywhere from a 7 to 28-day work period over which the overtime limit will be calculated. (See, e.g., 29 C.F.R. § 553.230 ["For those employees engaged in law enforcement activities ... who have a work period of at least 7 but less than 28 consecutive days, no overtime compensation is required under section 7(k) until the number of hours worked exceeds the number of hours which bears the same relationship to 171 as the number of days in the work period bears to 28"].). That the start and end dates within corresponding work periods and pay periods do not always align is a unique analytical component of Section 7(k) wage & hour analysis.
Section 7(k) also provides that “overtime pay is required when the number of hours worked exceeds the number of hours that bears the same relationship to 212 (fire) or 171 (police) as the number of days in the work period bears to 28.” This is an exemption to the FLSA requirement to pay employees overtime for hours in excess of 40 in a seven-consecutive day workweek. See U.S. Department of Labor Wage and Hour Division, Fact Sheet #8: Law Enforcement and Fire Protection Employees Under the Fair Labor Standards Act (FLSA), revised March 2011.
[2] The regular rate of pay is the rate used in calculating overtime wages and includes the employee’s hourly rate plus certain forms of remuneration, such as commission and incentives (if paid).
[3] The testing can be done before the information is converted to an electronic format (using the hard copies); this test, however, is performed more efficiently after the data has been converted.