United States: Give The Data A Chance: Identifying And Interpreting Behavioral Patterns In Wage And Hour Disputes

Last Updated: May 15 2014
Article by Dr. Alex Grecu

Employers often worry that by increasing the amount of data they track about employees' activities, they also increase the risk that these same data may be used against them in wage and hour litigation. There is a statistical basis for this concern. Large data sets increase the chances that relationships measured within these datasets will register as statistically significant, even when those relationships are spurious. That is to say, should an employer decide to collect data metrics regarding its employees' activities with an increased frequency/accuracy or expand the scope and detail of the gathered data to include details previously not collected, the employer increases the chances that, in the event of a dispute, the experts analyzing the data will find a significant statistical relationship where none existed with the more limited data available before.1 What employers may not realize, though, is that access to this more detailed data may be very beneficial in demonstrating that individualized circumstances and behaviors prevent class certification, in defending against meritless allegations, in winnowing down classes that are overly broad, and, where necessary, in generating a more accurate estimate of any damages. In fact, these benefits can be reaped efficiently, since an analysis made possible by the availability of extended data could be used during all stages of a wage and hours dispute, from class certification and addressing the liability through estimation of damages if needed.

Expanding the collection of data related to workforce activities can take many forms. Data regarding the start and end of the employees' daily activities can be collected with a higher frequency by splitting blocks of activities into shorter activities making up the larger blocks. For example, instead of just collecting the start and end time of a work shift for TV cable technicians, the employer may instead decide to register the start and end of the driving to, installation, testing, and customer training stages. Alternatively, it may be possible to expand the collection of data to categories of employees for which such data was not previously collected. Or, the employers may expand the data collection with the sole purpose of increasing the frequency from daily to per shift collection.

While there are risks of increased data collection, along with additional costs of such an extension, the potential benefits should be carefully considered. An important benefit that is often overlooked is that the data, and specifically more of it, allow an expert to identify patterns in employees' behavior and assess the strength of these patterns. Consequently, it is possible to determine categories of employees for which these patterns are representative and those for which they are not and/or conclude whether these patterns are in direct contradiction with the claims. In the context of wage and hour litigation, this benefit can be significant. Imagine a claim where individual employees allege that they were forced to work during their lunch break without any supplemental consideration from the company. Suppose that the company's policy allows for certain employees to work through part or all of the lunch break and instead leave earlier from work. Since this is a voluntary provision, some employees choose to work through their lunch break while others do not. Additionally, some employees may offset the time worked during the break to a lesser extent than the others, so that the amount of overall worked time becomes disputed. Tracking the times the employees begin and end lunch, or at least the start and end times of activities interrelated with lunch, in addition to the times in and out of the shift, can help deflect the allegations by assessing the extent to which the employees with shorter lunch periods also ended their shifts earlier. Even when the lunch begin and end times are absent, analyses of patterns based solely on shift start and end times could provide some helpful information, albeit not as extensive.

In and of itself, the extended data collection does not necessarily lead to benefits for employers. In many instances, the detailed exploration of employees' behavioral patterns may not be warranted. This is the case when all or the overwhelming majority of employees have an expected and perfectly predictable sequence and timing of activities during their work shift. In that case, the same single deterministic calculation can be applied to each employee in order to determine the length of their lunch break, for example. There is no need to study behavioral patterns in a situation when all subjects behave in the expected and similar way.

In certain cases, however, the very notion of whether particular patterns show in the data, how they look, and how to interpret them are crucial. Consider the case (Fig.1) when there is a dispute about whether employees followed a particular sequence of activities during the work shift and whether the time spent on each of those activities as measured by the data is commensurate with the claims made by the parties in the dispute. In this case, an examination of the distribution of the times when each employee has started the activity and ended it, and the superimposition of each activity next to each other, can identify the actual patterns followed by the bulk of employees.

Consider a company providing transportation services that employs a sizable number of drivers. The figure above shows the time-of-day distributions of each employee's four daily ID card swipes. As a matter of business, each driver must complete two routes per day. At the beginning and the end of each driving stage, each employee is required to swipe its ID card through a scanning device which records the scan's date and time-of-day. Assume that a few of these employees claim that they routinely worked through the lunch break and have not been paid accordingly. Because of this, they say the company owes them overtime. The height of each distribution shows the percent of all drivers' swipes occurring at a given time-of-day (indicated on the horizontal axis.) The close examination of these distributions leads to a few relevant discoveries:

  • There is evidence of an orderly time sequence of the drivers' swipes
  • There is evidence of a pattern in swiping activities as opposed to random and purely individualized behaviors
  • The apparent pattern shows that vast majority of drivers have been idle between approximately 11AM and 2PM
  • Most drivers complete the first route of the day between 6:15AM and 11AM, and the second route between 2PM and 6:30PM

These observations may prove valuable in defense against such claims. Together with legal and business related arguments, the identified pattern could be used by a defendant to argue that the time spent by drivers between the second and third swipes of the day is non-compensable. If the defendant prevails on this crucial principle, then a deterministic formula—in which the compensable time of each employee equals the sum of the differences between the first/ second and third/fourth swipes—can be applied. Ultimately, the compensable time can be compared to the time paid by the company to each of its drivers. In contrast, the common alternative scenario of computing the compensable time as the difference between the first and last swipes of the day will lead to vastly overestimated amounts. Thus, even if these data were not collected with the explicit goal of computing the compensable time for each employee, a savvy researcher can use it to infer useful and reliable pattern evidence.

The identification of behavioral patterns does not have to be limited to the time-of-day activities. It is possible to extend the analyses of patterns to weekly or seasonal fluctuations, determine patterns in behavior for the same employee over time or even compare the behavior among two or more groups of employees. The experience and diligence of an expert, along with the detailed knowledge of specific employment policies, will enhance the usefulness and reliability of the identified patterns. Note that any analyses of behavioral patterns can only be based on the observed behaviors of the bulk of the employees. This generally means that some employees may not follow the pattern identified for the majority of their co-workers. Because of this, it is possible for an expert to assess the strength of the observed pattern by examining the individual distributions' variation around the pattern's inflection points. The narrower the variation in a distribution, the more representative the pattern is for the employee population under study. As seen in Fig.1, although there is a discernible pattern, substantial variability in employee behavior remains. Such a consideration may be important in assessing the suitability, or lack thereof, of class certification.

An interesting exercise afforded by the availability of extensive time stamp data is the possibility of separating this variation in behavior specific to individual employees from the overall pattern observed across all or most other employees. Consider again (Fig.2) the distributions of swipes' time-of-day across all employees along with the swipe sequences for an individual employee - employee X. This employee ordinarily swiped for the first time at 10AM, for the second time around 2:30PM, for the third time at 3PM and finally for the last and fourth time at 8PM. Several observations can be made about this employee's behavior. Three out of four swipes lie on the outside of the bulk of the corresponding distributions for the rest of employees. In addition, it is apparent that the time difference between the second and the third swipes is rather small, unlike the typical length of the lunch break for most other employees. Such analysis of the distribution of the data for the entire population allows for an estimation of the normal variation from one employee to another. Any individual deviation beyond this normal variation can be identified as outlier behavior. This calculation has at least two advantages. First, the analysis of variation around the distributions allows for tests of statistical significance, meaning that any individual deviation can be tested to determine the likelihood of such a large deviation being generated by a completely unrelated random process. Second, the large deviations from the usual patterns of behavior can be further analyzed to determine the reason for its occurring. In case of employee X, it is apparent that the individual's behavioral pattern is uncommon and involves unusually short lunch breaks.

Instead, if the reason for identified outlier behavior proves to be a missing or erroneous data entry, it is possible to use the best estimate provided by the distributions to make inferences as to how the actual data entry most likely should have looked. Analogous to the above example, where the existing swipes are used to isolate the length of the lunch break, in certain cases the observed patterns can be used to "fill-in" gaps in the data. The information may be missing because of employee's failure to properly enter the information or by design of the recording system. Assume that for some drivers, the fourth swipe (which identifies the closing time of the last activity of the day) is missing because the employee forgot to properly swipe his or her ID badge. The end of the work day for these employees can be inferred using the information that the same worker has entered on other comparable workdays or use information entered by other employees in similar circumstances. If one finds filling-in the data undesirable, it is still possible to make certain qualitative statements regarding facts that could or could not have reasonably occurred, solely based on the observed pattern in the data.

Since the wage and hour claims can and often do impact large classes of employees, minor adjustments to the formulas used to calculate damages may lead to large fluctuation in damages estimates. The identification of behavioral patterns is therefore paramount for accurate damages estimation. In the example presented above, a not-so-careful expert may consider the start of the first activity as the start of the workday, and the end of the last activity the end of the workday. If this expert proceeds to assume that all the time between these two swipes is work time, the damages would be overestimated relative to the scenario where the expert makes an effort to first identify the activities patterns during the workday and appropriately remove all non-compensable time from the damages calculations. A blanket assumption that intra-day records of activities can be disregarded from damages calculations can and should be rebutted with a careful analysis of the patterns occurring during the day and a sound interpretation of those patterns. Therefore, even if the records collected during the workday do not identify the exact nature of employees' activity, the identification of the behavioral patterns can add to our understanding of time records and provide the logical basis for exclusion or special treatment of blocks of time in calculations of damages.


1 This is a statistical property intrinsic to large datasets which should not be confused with the practice of "multiple comparisons" when an unscrupulous expert assesses various comparisons within subsets of a larger dataset in the hope of finding a disparity within parts of the data.

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.

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