Emerging Strategies in the Evolving Area of Predictive Scheduling

For decades, the problem of scheduling has plagued employers and employees alike. Employees prefer predictable and reliable schedules. Employers, however, need flexibility.

To address this tension, regulators throughout the United States have recently begun to insert themselves into the struggle through the passage of predictive scheduling laws that seek to strike a tenuous balance between these competing interests. Given the recent rise in the popularity of these laws, it is important for organizations to understand what these laws are, where they are most likely to be encountered and what steps they can take to make stay abreast of the most up-to-date compliance strategies.

What are predictive scheduling laws?

Predictive scheduling laws are straightforward. In short, they require employers to post employee work schedules a certain number of days in advance of when the work is to be performed. Once posted, however, employers can be penalized for making any subsequent scheduling changes.

In theory, these laws seek to balance respective interests between employers and employees — a balance that was recently addressed in the landmark California decision, Ward v. Tilly’s. Therein, the court assumed the role of the employee’s champion and explained that, from the employee’s perspective, schedule predictability was an absolute necessity that allowed employees to, among other things, plan around second jobs, make child-care arrangements, coordinate school schedules and commit to social plans. Glaringly absent from this analysis, however, was the employer’s perspective and concurrent recognition that scheduling changes and fluctuating staffing needs are often caused by unforeseeable market realities, such inclement weather, employee call outs and unposted community events.

In practice, unfortunately, legislators have expressed wide disagreement over how to address this problem, causing many jurisdictions to take wildly different approaches. For example, in New York City, certain employers are only required to post schedules 72 hours in advance, with changes thereafter being completely prohibited. In contrast, San Francisco requires employers to post schedules not less than two weeks in advance. Once posted, however, any changes require the employer to pay the affected employee anywhere between one and four hours of additional “predictability pay,” depending on how last minute the change actually was. As these examples demonstrate, the lack of a centralized model for predictive scheduling laws has created a potential minefield for employers who attempt to apply consistent scheduling practices in multiple jurisdictions.

What industries and jurisdictions have been most affected?

Since the first predictive scheduling law arose in San Francisco in 2016, other states and major U.S. cities have contributed to a precipitous rise in these laws. Places like Vermont, Oregon, New York City, Chicago, Seattle and Philadelphia have all proposed and implemented their own unique frameworks. Simultaneously, other states have actively sought to combat the rise of these practices. In the wake of San Francisco’s law, states like Arkansas, Iowa, Georgia and Tennessee quickly implemented legislation that prohibited their own major cities from enacting similar predictive scheduling laws, seeking to stifle an already emerging trend.

To date, however, the retail and hospitality industries have taken the brunt of the regulatory force, with the vast majority of predictive scheduling laws targeting these industries exclusively. As justification for this disparate treatment, legislators have pointed to the disproportionate number of low-wage workers present in these industries who, by their nature, warrant greater protection. For these employees, securing a reliable schedule through traditional means, such as direct negotiation, is far less likely. Accordingly, in these industries, employer/employee tension between scheduling flexibility and predictably is at its zenith.

Unsurprisingly, established players in these industries have already begun to feel the impact of these new scheduling strictures. For example, in September 2019, the New York City Department of Consumer and Worker Protection filed a substantial lawsuit against certain private businesses for various compliance failures, such as:

1. failing to meet advanced posting requirements;
2. failing to provide “good faith” estimates of upcoming scheduled hours;
3. failing to obtain employee consent before enacting scheduling changes; and
4. failing to pay employees their required pay premiums when last-minutes changes did occur.

As a result, the lawsuit alleges nearly $1 million in prospective liability amidst a combination of employee restitution and state penalties — a figure that’s certainly nothing to balk at.

So, what should you do now?

Unfortunately, compliance with predictive scheduling laws is far from easy.

Although larger employers with locations throughout multiple jurisdictions tend to be the most affected, smaller employers can also find themselves in a position that requires a full overhaul of their current staffing model. Accordingly, it’s important to keep a few points in mind:

1. Audit Your Locations. Predictive scheduling laws don’t apply to everyone. Although standards continue to evolve, in most jurisdictions, predictive scheduling laws will only apply to employers in particular industries that reach a particular size. As a result, you should first determine whether a particular location qualifies as a “covered employer” under the prevailing local standards and understand what laws apply to that specific location.
2. Build the Infrastructure. If predictive scheduling laws do apply, you should begin to build compliance tools into the infrastructure of your human resource systems. Standardized forms of documentation for things such as “good faith estimates” of upcoming work hours, scheduling change notices, scheduling change consent forms and work schedule acknowledgements can all be helpful to have on hand. And, like most compliance efforts, documentation and data storage can be critical in staving off allegations of impropriety.
3. Tailor Your Policies. The piecemeal framework of predictive scheduling laws throughout the country means that you may also have locations that are subject to different predictive scheduling requirements. As a result, a centralized staffing model can quickly become outdated or, even worse, a potential liability. Location-specific policy changes may need to be made, and managers may require training on how to handle staffing shortages moving forward.
4. Consider Novel and Creative Approaches. To address the rise of these laws, several large companies have implemented the use of scheduling apps. In addition to viewing pre-posted schedules, employees can use the apps to swap shifts with fellow co-workers or even sign up for unfilled shifts in upcoming weeks. However even without apps, voluntary schedule swapping and sign-up policies are both phenomenal ways to reduce and even eliminate the need for last-minute scheduling changes, all while boosting employee morale.
5. Avoid Related Pitfalls. No employment law exists in a vacuum, and predictive scheduling laws are no exception. Implementing predictive scheduling models will often impact other aspects of your business and, in some cases, can create unforeseen liability traps. For example, in San Francisco, forgetting to tell your payroll company to separately delineate the “predictability pay” scheduling change penalty on your employees’ wage statements could possibility saddle you with potential labor code violations if a court decides to consider it a type of “wage” — all for a simple oversight.

Conclusion

In the modern day, employment laws are changing at an ever-increasing pace. Additionally, when it comes to employment policies, there is rarely a one-size-fits-all approach, and what’s right for one company may not be right for another. As a result, it is important to keep up to date on the newest changes in both law and compliance strategies.