Literature Review: Data in wage and hour litigation, from NERA
NERA’s article exploring the role of data in wage and hour cases introduces the reader to the statistics that may be used to evaluate class certification or calculate damages in wage and hour cases involving missed meals and breaks, off-the-clock work, improper termination pay and overtime misclassification.
‘Data in Wage and Hour Litigation: What to do when you have it and what to do when you don’t‘ is written by NERA economic consultants Dr. Elizabeth Becker, Dr. Alex Grecu, Sukaina Klein, Dr. Denise Martin, Mary Elizabeth Stern and Dr. Kent Van Liere.
Data is at the core the statistical analysis in a wage and hour case, and it is often varied and complex. It can include any combination of: timekeeping records, payroll data, cash office records, sales records, scanning device data, computer and phone logs, email communication, client management system data and calender entries. Every case is different, as is the data. This highlights the importance of working with an economic and statistical expert who will approach the analysis with reliable methodology and assumptions.
Employer’s records do not always provide the information required for a reliable statistical or economic analysis, which can lead to unreliable analysis results is formulaic approaches are used. Two case examples illustrate how employer records that are ‘complete’ in terms of the law, are not necessarily complete data sets appropriate for statistical analysis of the specific allegations.
The authors warn us that formulaic analysis of business records can generate unreliable results. Company records that are accurate for business purposes can generate misleading results when used to answer questions in litigation.
Rigorous statistical analysis is necessary to determine if relevant datasets can be used to produce reliable results, as highlighted with “a simple comparison of two datasets maintained for business purpose produce an apparent violation when no actual violation occurred.”
Incomplete business records may yield insights useful in class evaluation. The article uses an administrative exemption case example, in which pharmaceutical sales reps allege they performed overtime work that should have been paid.
The authors then turn their attention to exploring what can be done in the absence of data.
First, they turn their attention to surveys and samples used in wage and hour litigation. Surveys are conducted according to widely accepted procedures, with questions customized to fit the particular lawsuit. Uniform data can be collected from sample members eliminating the need for costly and time consuming data collection from every class member.
A number of survey biases are identified that may jeopardize the quality of a survey in a wage and hour suit. Answers that rely on a respondents’ ability to recall events or dates or require difficult calculations may be of questionable accuracy. Because most sample surveys have less than 100 percent participation, special attention must be paid to nonresponse bias. A survey example and analysis is provided to illustrate their points.
They also touch on the risk of bias in samples and its impact on the reliability of expert testimony when a sample of existing data is used to draw conclusions about the class as a whole.
Variability in the data is discussed and the authors suggest it indicates further investigation is needed to assess if any class-wide violations occurred. Finally, the authors illustrate how variability in survey and sampling results may be used to argue against class certification.
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