Data in Wage and Hour Litigation:

Transcription

1 4 November 2010 Data in Wage and Hour Litigation: What To Do When You Have It and What To Do When You Don t By Dr. Elizabeth Becker, Dr. Alex Grecu, Sukaina Klein, Dr. Denise Martin, Mary Elizabeth Stern, and Dr. Kent Van Liere Introduction Wage and hour cases often require an assessment of whether data available from business records can be used to evaluate class certification or estimate damages. In situations where no even arguably relevant data exist, questions may be raised about whether sampling and survey techniques can be used to remedy these gaps. Below, we discuss how the application of statistics can address these issues, using examples that have arisen in a variety of wage and hour settings, including allegations of missed or short meals and breaks, off-the-clock work, improper termination pay, and misclassification/failure to pay overtime. What Can We Learn from Existing Electronic Business Records? A variety of electronic data or records maintained in the ordinary course of business may be available from defendant companies, including: Timekeeping records Payroll records Cash office records Cash register transactions or other sales records Hand-held scanning device logs Computer logs Phone logs Expense logs s Security systems Client relationship management system entries Electronic calendar entries for meetings or calls

2 Such electronic data can be analyzed statistically, and in combination with other company records that may not be maintained electronically, to assess whether it allows conclusions to be reached regarding class certification or damages. Application to Meals and Breaks, Off-the-Clock, and Termination Pay Cases Plaintiffs experts frequently proffer conclusions at the class certification or damages phases that are based on formulaic analysis of existing business records. However, statistical analysis may show that these records, while accurate for business purposes, do not provide information needed to answer the questions raised in litigation, and that any formulaic approach may generate incorrect or otherwise unreliable results. Business Records May Not Provide Information Required for Litigation Consider a case where employees allege they were not allowed to take full 30-minute meal periods. Plaintiffs experts may attempt to use the timekeeping records to identify any meal periods that are less than 30 minutes and assert that each instance represents a situation where the employee was involuntarily required to return to work early from his/her meal. However, a statistical review of the timekeeping data might reveal that while many meal periods are exactly 30 minutes long, employees frequently swiped in or out for periods both slightly shorter and slightly longer than 30 minutes. See Figure 1 for an example of employees recording meal breaks between 20 and 40 minutes long. Figure 1. Meal Periods by Duration, 20 Minutes through 40 Minutes 140, , ,000 Meal Breaks Taken 80,000 60,000 40,000 20, Length of Meal (minutes) 2

3 In the chart above, over 95% of employees are shown to have swiped in or out within five minutes of the 30-minute mark. While the short meal periods may have been shortened involuntarily, because many meal periods longer than 30 minutes also exist, this type of statistical review instead suggests that such patterns are evidence of imprecision in swiping by employees. Without additional information, an expert cannot discern from the electronic data alone the reason for the meal periods shorter than 30 minutes, and whether the observed swiping patterns indicate any violation of state or federal laws. Instead, individualized review would be required. To take another example, consider a case alleging that employees were not paid in full at the time of termination. An analysis of electronic timekeeping records and payroll records may be put forth by plaintiffs expert showing amounts purportedly owed but unpaid at the time of termination. However, electronic records may not tell the whole story. A review of a sample of personnel files, generally maintained in paper form, might reveal class members not in the store at the time of termination had failed to return to the store to collect their final pay and did not respond to repeated attempts at contact by the company. A statistical review of the amounts allegedly owed to these employees might show that they were very small, providing a potential explanation for why those employees chose not to return to the store to collect the payout. A manual review for each proposed class member could be costly, both in terms of time and expense, and there is a significant likelihood that some personnel records may be incomplete. However, statistical analysis of the larger class might reveal that a large proportion of employees identified by plaintiffs formulaic approach as having been improperly paid upon termination were similarly owed small amounts. These statistical findings can be used together with findings from a smaller sample to argue that these employees may also have failed to respond to attempts at contact. Such findings demonstrate that individualized review is required to understand the facts and circumstances surrounding the alleged failure to pay termination wages. Formulaic Analysis of Business Records Can Generate Unreliable Results Consider next a case where plaintiffs allege they were forced to work off-the-clock through their meal periods. Plaintiffs experts might attempt to use a variety of business records to find evidence of this off-the-clock work. For example, using cash register and time-keeping records, plaintiffs experts might try to identify the employees whose IDs were shown to be operating the cash register when the same IDs had been used to swipe out of the timekeeping system. However, this comparison can generate meaningless results in a variety of circumstances. For example, suppose an employee had taken a meal period from 1:30 to 2:30 PM but had forgotten to swipe. When reporting the omission to his manager, however, suppose the employee misremembered and told the manager the meal period had been from 12:30 to 1:30. Figure 2 depicts the results of a comparison of timekeeping data and cash register data in such a situation. 3

4 Figure 2. Comparison of Swipes with Meal Period Inserted at the Wrong Time Time Clock Swipes Cash Register Swipes 9:00 am Swipe In 9:05 am Swipe In Transaction data 12:30 pm Swipe Out 1:25 pm Swipe Out 1:30 pm Swipe In 2:35 pm Swipe In Transaction data 5:30 pm Swipe Out 5:25 pm Swipe Out Comparing the two data sets above, an expert might conclude that the employee had a 55-minute period of off-the-clock work from 12:30 pm through 1:25pm. However, if the data were corrected to insert the meal period at the proper time, the apparent off-the-clock work disappears. Figure 3 shows the results of a corrected comparison. Figure 3. Comparison of Swipes When Meal Break is Correctly Inserted Time Clock Swipes Cash Register Swipes 9:00 am Swipe In 9:05 am Swipe In Transaction data 1:30 pm Swipe Out 1:25 pm Swipe Out 2:30 pm Swipe In 2:35 pm Swipe In Transaction data 5:30 pm Swipe Out 5:25 pm Swipe Out It is important to realize that, in this situation, regardless of whether the meal period was inserted at the correct time in the timekeeping system, the employee was paid correctly. She should have been paid for a 7.5-hour shift, and not paid for a 60-minute meal period, which is what is shown to have occurred in either Figure 2 or Figure 3. The company s records, while accurate for business purposes, generate misleading results when they are used for litigation purposes. Such situations are not uncommon. Meal period insertions or other edits to the timekeeping system are likely to occur regularly and, even if just a few minutes off, can create the appearance of off-the-clock work where none existed. Similarly, clocks across different systems may not be synchronized due to different time zones, daylight savings time, power outages, battery failures, or other reasons. Statistical analysis showing that the duration of alleged periods of off-the-clock work are generally only a few minutes can provide evidence consistent with such asynchronicity and suggest that periods of alleged off-the-clock work may not have existed. A statistical review of outliers in the data may suggest other reasons that a formulaic review of business records may generate unreliable results. For example, a review of cash register logs may reveal that some employees appear to have been logged on to cash registers for more than 24 hours at a time. See Figure

5 Figure 4. Cash Register Swipes Lasting Over 24 Hours Cash Register Session Start Date Session End Date Length of Shift 1 3/25/2005 3/28/ hrs and 26 mins 2 10/1/ /3/ hrs and 18 mins 3 10/23/ /25/ hrs and 6 mins 4 1/22/2005 1/23/ hrs and 22 mins 5 4/21/2005 4/22/ hrs and 55 mins 6 5/4/2005 5/5/ hrs and 19 mins 7 5/6/2005 5/7/ hrs and 13 mins 8 5/18/2005 5/19/ hrs and 55 mins 9 10/8/ /9/ hrs and 14 mins 10 9/17/2005 9/18/ hrs and 47 mins One explanation for such occurrences, which may be corroborated by interviews and testimony with employees and their managers, is that employees use one another s IDs to operate the cash register. If so, it is unreliable to assume that off-the-clock work can be identified by locating periods when an employee s ID is shown as operating a cash register when that same employee is not shown as being logged into the timekeeping system. What appears to be off-the-clock work may simply be a second employee (one who is logged into the timekeeping system) operating the cash register under the ID of the first employee. While such problems are revealed through an analysis of statistical outliers (such as shifts exceeding 24 hours), simply excluding such outliers from any calculation of alleged off-the-clock work does not remedy the unreliability of the approach. The existence of such anomalous entries indicates that the cashier logs cannot be used to identify periods of off-the clock work. In situations such as those described above, a simple comparison of two datasets maintained for business purpose produce an apparent violation when no actual violation occurred. These examples highlight the need for rigorous statistical analysis to determine the appropriate way, if any, to link relevant datasets and to assess that the results produced are reliable. Application to Exemption Cases As shown above, business records may be inadequate to identify the hour-to-hour or even day-to-day activities. However, a compilation of data from various sources may still yield insights useful for evaluating whether a class should be certified. This proposition can be understood through an example from an administrative exemption case, where pharmaceutical sales representatives allege that they should have been paid overtime. A key qualification for the administrative exemption is that the employee exercise discretion and independent judgment with respect to matters of significance. Statistical analysis of existing data can be used to assess the exercise of discretion. Figure 5 details the type of data that might be available in a case involving pharmaceutical company sales representatives. 5

6 Figure 5: Data Sources to Evaluate Sales Reps Activities Date and Database Contents Time Stamped? Calls on Database that records the date, location, Date Stamped Physicians and time of physician visits, by sales representative Samples Type and number of samples given by a sales Time and Dropped rep to a physician during each visit Date Stamped Human Records of the hire date, termination date, and Dates of Resources relevant status changes in base salary, home Status Changes address, etc. Payroll Dates and amounts of compensation, by category Payment Dates s Text and metadata on s sent and received Time and Date Stamped Sales Sales rep s performance goals, % of goal achieved, Performance and group ranking No Speaker Dates and locations of speaker programs Date Stamped Programs organized by sales rep Promotional Types and quantities of promotional materials No Materials used by the rep In this setting, properly compiled data can be used to demonstrate variability of behaviors, a finding which is valued both for opposing class certification as well as in defense of the exercise of discretion for the administrative exemption. Variability of behavior in the purported class can be demonstrated either by using simple descriptive statistics or with more sophisticated hypothesis testing and econometric analysis. Simple descriptive statistics across relevant organizational entities or across time can be used to show, for example, variance in daily, weekly, or monthly behaviors or differences in the mix of activities. For example, Figure 6 provides a graphic description of differences in sales representatives behaviors, specifically how the average weekly visits to physicians varies by division. The blue bars show the average number of physician visits made by sales reps in each division, demonstrating variability across divisions. The bar within each blue line shows the variability within any particular division. 6

7 Figure 6. Dispersion in the Average Number of Weekly Physician Visits Average Number of Visits per Week Oncology Renal Neuroscience Metabolic Cardiovascular Division Variability in behavior within the context of a visit can also be documented. For example, some representatives might be much more likely than others to provide samples of pharmaceutical products. (See Figure 7.) Such a finding would belie the argument that they were all given the same instructions as to how to perform the job and thus acted in a common manner and without discretion. Figure 7. The Proportion of Visits That Involved Sampling Neuroscience Oncology Cardiovascular 13% 0% 37% 63% The data can also be evaluated with more sophisticated statistical and econometric tests of the degree of individualized behaviors, depending on the degree to which institutional factors such as the division or sales force the employee reports to, the time period during which he or she was in the market, and his or her experience as a rep can be accounted for. After having considered the influence of these employer-specific factors, the remaining influence of the employeespecific behaviors can be tested. A significant effect of the employee-specific behavior provides a statistical test of variance among the reps. 7

8 Figure 8: Employee-Specific Effects Are Statistically Significant Predictors of Behavior Incremental Factor Considered Overall Description Variation 1 Week Division Force Experience Employee Unexplained (1) (2) (3) (4) (5) (6) (7) (8) Sales Number of Factors N/A Sum of Squared Errors Remaining 30,000 18,000 12,000 11,000 10,000 2,000 2,000 Explained Sum of Squared N/A 12,000 6,000 1,000 1,000 8,000 N/A Errros by the Incremental Factor R-Squared N/A Share of Explanatory Power N/A 40.0% 20.0% 3.3% 3.3% 26.7% 6.7% Attributable to the Factor F-Statistic Probability Value Notes: 1 Represents the overall variation in the number of physician visits from one week to another across employees [ ] ** Such data can also be used to challenge arguments that a job is so simplistic that it can be conducted without the need to exercise discretion. For example, if success in performance of the job can be achieved with different types of employee behavior, it suggests that the simple act of just showing up and making calls may be inadequate as an explanation of success. Reps making many calls could be unsuccessful and those making few calls could be highly successful. Such a finding would suggest that the content of the communication during the call is as important as the call itself. The data may also evidence a diminishing return to making additional calls, indicating that the quality of the contact with the doctors matters in addition to the sheer number of calls made. (See Figure 9.) 8

9 Figure 9. Diminishing Returns to Routine Activity Indicate Need to Exercise Discretion to Achieve Goals Relative Performance Ranking Visits per Month Poor Quality of the Fit Also Evidence of Variability in Behavior Last, evidence of learning by doing can show impact. The traditional economic evidence of learning by doing is a convex-downward earnings profile, as shown in the following chart. Figure 10. Human Capital Acquisition Indicates On the Job Learning $200,000 $180,000 $160,000 Total Compensation $140,000 $120,000 $100,000 $80,000 $60,000 $40,000 $20,000 Compensation rises with years of experience, but at a declining rate $ Years of Experience as a Sales Representative 9

10 What Can We Do in the Absence of Data? An interesting trend in wage and hour litigation is the use of surveys and samples. The appeal of a survey is simple: asking a representative random sample of employees questions about their work experiences seems to be an efficient method to collect information that may be relevant to a particular matter and is otherwise unavailable. More specifically: There are widely accepted procedures for conducting surveys that are recognized and accepted by the courts; The same data can be collected from sample members thus facilitating the aggregation and analysis of results (as opposed to reviewing and characterizing 50 deposition transcripts, for example); Survey questions can be modified or customized to fit the particular issues in a given case thus assuring probative data if properly done; and The sampling strategy can be designed using principles of clustering and stratification to assure that key groups are represented appropriately in the sample. However, asking employees about work experiences is potentially difficult and biases may result. Below, we demonstrate how biases introduced by a poorly designed survey could render it useless, whereby any results from the survey could not be extrapolated to the purported class. We also show that working with samples of data that may be available (e.g., in situations where data are available electronically for a portion of the class period, or no electronic data exist and counsel decides to input data for a sample selected from hardcopy files) can also lead to bias. Here again, the results from such samples may not be reliably extrapolated to the class as a whole unless the sample meets certain criteria (e.g., that the sample is sufficiently stratified so that all relevant segments of the population are represented). Finally, if either a survey or sample reveals a high degree of variability among the purported class members, such results may provide evidence that no class should be certified at all. The Risk of Bias in Surveys The results of a survey hinge on the quality of the data collected. A number of biases may result from the use of surveys and can jeopardize this quality: Surveys regarding work experience often rely on the respondents ability to recall events, dates, or specific behaviors. Recall is often imperfect and depending on the amount of time that has past and the memorability of the events the quality of answers may be weak. Certain kinds of questions may be subject to potential bias. For example, academic research has shown that stylized questions about work activity may be upwardly biased when compared to time diary information or comparisons to time clock data (i.e., people recall working longer hours than they actually did). Questions that matter may require difficult calculations such as estimating the average number of hours worked in a week. Many individuals have difficulty with questions involving too much math and the resulting answers can be nonsensical. 10

11 The possibility of so-called strategic responses must be evaluated. For example, some employees may realize that certain answers benefit them and they will skew their answers in that direction even if that does not fit their actual experience. Most sample surveys do not have 100 percent participation so attention must be paid to evaluating the extent to which nonresponse bias exists in the data (e.g., do the non-responders share common characteristics that are different from those of the participants?) Consider an example where employees allege they were asked to work off-the-clock. The alleged class period extends over seven years ( ), involving as many as 5,000 proposed class members, from 40 stores located throughout California. The following survey was mailed to 500 employees chosen randomly. Figure 11: Wage and Hour Class Action Questionnaire One useful feature of this survey is that it asks for a piece of historical data (the period of employment) that can be cross-checked against company records to shed some light on the recall ability of the respondents. However, there are a number of basic survey flaws: The purpose of the survey is clear. While it may not be possible to avoid potential class members knowing about the litigation, the survey should at a minimum be sent from an independent third party rather than the plaintiffs law firm. Moreover, assuming an expert is going to analyze the results along certain metrics, it is important that the expert also be involved in the survey design and execution. The survey requires employees to have very specific recall about many days over many years, some of them some time ago. Evidence shows that people remember yesterday well, and they remember unusual events well, but that their recall of day-to-day activities is not particularly accurate. Moreover, they tend to remember extreme circumstances, which can lead them to exaggerate their responses. 11

12 The survey insists on specific recall not offering I don t remember as an option. Studies show that this option is chosen more than a quarter of the time when it is offered. Because people don t have specific recall, they have a tendency to round, and many answers will be clustered at five minutes or 10 minutes in surveys of this type. Another important consideration in surveys is the nature of the respondents and whether they are likely to be representative of the class. Suppose of the 500 surveys mailed, only 100 were returned, yielding a 20% response rate. Lower response rates increase the probability of non-response bias those potential class members who do not bother to return the survey are less likely to have experienced a problem. Another consideration is whether the responses themselves were concentrated in some way. Suppose that while the complaint alleges the practice of asking employees to work off the clock is pervasive, all of the responses were concentrated in just eight of the 40 stores. Such concentration can be used to argue that the proposed class definition is overly broad; while there may be an issue at a few stores, there is no evidence of a corporate-wide issue. Figure 12. Were Responses Spread Evenly Across the Eight Stores With Any Response? Survey Non-Respondents 80% Survey Respondents 20% Further cause for concern about the reliability of the sample would be raised if even within the eight stores, there were concentration and variability. Suppose 50% of the 100 responses came from just two stores, where the average period of alleged off-the-clock work was 15 minutes, while the average time at the remaining six stores was just five minutes. Figure 13. Significant Variation Even Among Respondents 50% of All the Respondents Come from Just Two Stores Average Survey Response At These Two Stores Was 15 Minutes Average Survey Response At Other Responding Stores Was Five Minutes Figure 14. Expectation that Non-Respondents Are Even Less Similar Non-Respondents Average Survey Response At These Two Stores Was 15 Minutes Five Minutes? 0 Minutes? 12

13 50% of All the Respondents Come from Just Two Stores Average Survey Response At Other Responding Stores Was Five Minutes Average Survey Response At Given These Two the risk Stores of non-response Was 15 Minutesbias, the results of this sample cannot be reliably extrapolated to the proposed class. There is no way to conclude that the experience of the employees who responded to the survey is representative of the proposed class. Figure 14. Expectation that Non-Respondents Are Even Less Similar Five Minutes? Non-Respondents Average Survey Response At These Two Stores Was 15 Minutes 0 Minutes? Average Survey Response At The Other Stores Was Five Minutes The Risk of Bias in Samples In situations where data exist, but are not readily available for the class as a whole, experts may attempt to extrapolate from a sample of existing data to the entire class. For example, in a case where electronic records are only available for a portion of the alleged class period, an expert might assume that the overtime worked during the entire class period mirrored the overtime hours observed in the electronic subset. However, if the available data were to be examined by store and by time period, significant variability may be shown to exist. For example, variability may be evident: in the amount of overtime worked by employees in the sample, e.g., between stores in commercial and residential neighborhoods, where the hours of store operation were different; between periods in which the proportion of full-time and part-time employees may have changed, such that more overtime was worked in some sub-periods than others; or between managers, in which rather than being wide-spread, only those employees working for a handful of managers had not received the overtime to which they were entitled. Variability in the data suggests that further investigation is needed to assess whether any classwide violations had occurred, and extrapolation from a sample that does not take into account this variability can lead to erroneous conclusions. Other issues to consider when extrapolating from a sample to the class include what margin of error or confidence interval in an estimate is reasonable to the courts (which would affect the size of the samples required), and potential biases introduced by incomplete lists or sampling frames used in drawing the samples. Relying on nonrandom samples exacerbates these issues substantially. 13

14 Variability in Survey or Sampling Results May Be Used to Argue a Class Cannot Be Certified It may be that the survey or sample reveals sufficient variability in the purported class such that it cannot be certified. Consider a case where current and former employees bring a collective action under the FLSA related to the Assistant Store Manager (ASM) position, alleging misclassification of exempt versus non-exempt and failure to pay overtime wages. The action is filed as collective action; the court conditionally certifies the class; and 2,000 ASMs opt into the class. Suppose plaintiffs retain an expert to conduct a survey of all opt-in plaintiffs about their job duties as ASMs. They might be asked, for example, whether they regularly hire employees, terminate employees, set the schedules of other employees, discipline other employees, or promote employees. Plaintiffs expert will attempt to use results from the survey to support class certification by demonstrating that a large proportion of the putative class do not have these responsibilities. However, detailed review of the data resulting from the survey may show that varying percentages of the respondents agreed with statements about their job duties (e.g., 40 percent may indicate they regularly hire associate employees, 25 percent regularly terminate others employment, and 70 percent regularly set the work schedules of other employees). This variation may be presented as evidence that there was in fact variation in the responsibilities of the opt-in ASMs. Further, the data may be separated into groups to assess whether management responsibilities vary by other factors, e.g., tenure. If so, such findings can be used as further evidence that the class, as originally certified, did not have similar circumstances with regard to classification of job duties. Conclusion Because many companies maintain electronic records related to employees activities, it is tempting to assume that these data must be useful in wage and hour litigation. However, as the examples above show, such an assumption may be incorrect. A detailed statistical analysis may reveal that existing business records either are missing relevant information and/or that any formulaic analysis can lead to unreliable results. In such situations, the Court may determine that the class cannot be certified or that the electronic databases are not sufficient from which to estimate damages. Statistical analysis of such records may also reveal significant variability either in patterns of alleged violations or in employee behavior. Variability in patterns of alleged violations, such as the proportion of rest breaks that appear to be taken, may indicate that other innocuous factors explain what appears to be a violation under a formulaic analysis. Variability in observed employee behavior, such as the proportion of pharmaceutical rep visits where product samples are provided, may provide evidence that a class cannot be certified and provide a defense of the exercise of discretion for the administrative exemption. Finally, the examples above show why care must be taken in survey and sample design and extrapolation, and how variability in sampling results can itself be used to argue against class certification. 14

15 Contact For further information and questions, please contact the authors: Dr. Elizabeth Becker Senior Vice President Dr. Alex Grecu Senior Consultant Sukaina Klein Consultant About NERA NERA Economic Consulting ( is a global firm of experts dedicated to applying economic, finance, and quantitative principles to complex business and legal challenges. For over half a century, NERA s economists have been creating strategies, studies, reports, expert testimony, and policy recommendations for government authorities and the world s leading law firms and corporations. We bring academic rigor, objectivity, and real world industry experience to bear on issues arising from competition, regulation, public policy, strategy, finance, and litigation. NERA s clients value our ability to apply and communicate state-of-the-art approaches clearly and convincingly, our commitment to deliver unbiased findings, and our reputation for quality and independence. Our clients rely on the integrity and skills of our unparalleled team of economists and other experts backed by the resources and reliability of one of the world s largest economic consultancies. With its main office in New York City, NERA serves clients from more than 20 offices across North America, Europe, and Asia Pacific. Dr. Denise Martin Senior Vice President denise.martin@nera.com Mary Elizabeth Stern Vice President mary.elizabeth.stern@nera.com Dr. Kent Van Liere Senior Vice President kent.van.liere@nera.com The opinions expressed herein do not necessarily represent the views of NERA Economic Consulting or any other NERA consultant. Please do not cite without explicit permission from the author. Copyright 2012 National Economic Research Associates, Inc.