So why don’t we? Because “data-driven” decisions are still biased.

Evidence in support of a four-day workweek is overwhelming. It worked for the Icelandic government. It worked at 23 different companies across the U.K. It worked in Spain. The data are undeniable. When people reduce the number of hours per week they work on average without a reduction in pay, they are happier, less likely to quit, and are equally or more productive.

The biggest complaint people seem to have with studies and trials related to the four-day workweek is that the name is misleading. While many businesses cut one full work day out of the business week, some reduced hours in other ways. For example, in some industries, it doesn’t make sense for everyone to simply not come to work on Friday, so working hours were reduced in other ways, such as working shorter hours every day or working severely reduced hours during a slow season, such as in the case of restaurant workers in the U.K. trials. (Tomato, tomato.)

If all signs point to improved results for both employers and employees, why haven’t more businesses adopted or at least run a trial for a four-day workweek?

One of the reasons is all data-driven decisions are still biased. When we analyze any information with the express purpose of either making a change or making no change, decision-makers have to think about what they value. What matters most now? Do they value short-term profits over long-term profits? Is increasing employee retention important to them? What is their tolerance at this very moment for disrupting business as usual to potentially increase productivity?

You can see data bias in action whenever layoffs happen. Cutting large numbers of employees from a business is extremely expensive in the long term and detrimental to operations (and employee morale) in the short term. But what do the decision-makers value most right now? If the answer is “showing a decrease in the cost of payroll this quarter,” then layoffs “make sense.”

As much as “maximizing profits” or “maximizing shareholder value” might always be the explicit bottom line, there are always other biases, often implicit, that go into making decisions.   

Image by Manasvita S on Unsplash