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Time-Clocks, Employee Swipe Behavior and Implications for Wage and Hour Litigation

In recent years so-called wage and hour issues (as distinct from traditional labor discrimination matters) have emerged as the employment litigation of choice for the plaintiffs' bar. Searching Google for "wage and hour" with the name of your (least) favorite large company will likely return several hits. Indeed, nearly every corporation in the US with a large workforce is susceptible to multiple lawsuits under both federal and state laws, of varied scopes and types under the wage and hour umbrella. This is particularly the case if a company operates in states like California - the state with the toughest wage and hour protections laws. There are many varieties of wage and hour issues but perhaps the most litigated issue has to do with meal and rest break violations.

The attraction of lawsuits of this kind stems from two primary sources. First is the tremendous amount of monetary damages in the offing for plaintiffs (and plaintiffs' attorneys) if a litigation or mediation outcome is reached in plaintiffs' favor. These damages are routinely estimated in the many tens of millions of dollars, if not substantially more. Second, the ready availability of huge amounts of heretofore unworkable data and the computing means to efficiently, and accurately, analyze this data makes this sort of litigation feasible for plaintiffs to bring.

Our effort here, while acknowledging the primary incentive of the first cause, is to delve particularly into the second - looking specifically at the data analysis possibilities presented in analysis of meal and rest break violations, and resulting penalties among hourly workers. We attempt to analyze whether counting meal and rest break violations - and generating damages on the basis of this count - in the manner proscribed by statute, passes social science muster. Specifically, we look at whether actual time clock punch behavior, in combination with different standard deviations thresholds would substantially alter the counts of violations generated and the resulting damage estimates.