Salary Transparency
As we go through this “Great Resignation,” or as LinkedIn has now titled it the “Great Reshuffling,” people are applying for jobs like crazy. But this is brought an interesting issue to the forefront that really never has been discussed at high levels. That is until now and some states are even addressing it with legislation.
While several state and local jurisdictions previously had implemented the policy it seems when something finally reaches New York City, it achieves the highest levels of media attention. The New York City Human Rights Law, with one of its recent amendments, will require that all employers, including nonprofits, include a minimum and maximum starting salary for any job that is advertised. Colorado has implemented such a law. The New York State government is considering it. Maryland, Pennsylvania, and Washington are in the same boat.
The goal is to reduce the pay gaps based on gender and race. Basically, if you post it then you know what the limits might be. This of course does not illuminate someone based on non-job skills being paid less in a certain range, but the principal is is that if it’s public there will be more equity.
I’m not against it in principle. It does create some interesting scenarios for people inside the organization who might already work there and see other positions paid the same or more---maybe causing resentment. It might also have a positive unrealized effect of finally having important conversations that fight the notion that just because were the nonprofit world, we shouldn’t be paid as well as the for-profit world for similar jobs. And for those incredibly small nonprofits, it might have a negative impact when people realize they can’t afford to pay people a living wage, causing negative outcomes with donor and community relations.
In the end, I think this is going to be prevalent in many, many cities and states. In fact, it most likely will become the norm. In the end, it’s probably a pretty good idea even if all of the details and unrealized outcomes aren’t completely understood.
