Inequity breeds resentment
Pay rates in organisations are often treated as a dirty little secret. The story is old: Managers don't like their subordinates to know how much more they get paid, and people at the same "level" get paid different rates based on "market negotiations". Relative deprivation and inequality are well-documented as issues in our social fabric, often more than than actual poverty, and based on experience, the same holds in our organisations - especially if there's a multiplier factor between the hidden millions of the C-Suite and the on-ground teams.
Discrimination inherent in pay negotiations
We also know that individual wage negotiations preferences certain groups (read: narcissists and powerful white guys who are ok with conflict), and often marginalises groups who are already at the fringes of power.
An algorithm as a response to inequity
So when we heard that the on-ground staff at one of our clients had some interpersonal conflict and in-team resentment based on ad-hoc rates of pay, we saw an opportunity to practice the organisation's new values of "fairness" and "transparency".
In partnership with our client, we built a prototype algorithm to pay staff. I should note that we had already developed a Developmental Pathway with options for staff to add specialisations as they stayed and learned. Our aim was to financially reward learning and tenure in order to offer longer-term options for staff (turnover was very high when we started).
Needs from the algorithm process
We brought in our statistician and she developed a line of best fit for current wages and skills in the team. The algorithm had to meet the following needs:
1. It had a standard incoming 3-level pathway (every new team member progressed from Novice > Junior Trade > Certified Trade)
2. It rewarded tenure, with increasing retention being the overall aim of several initiatives
3. It incrementally added payment bumps for competencies as staff added specialisations (and were more "valuable" and experienced as a result)
4. The organisation had the ability to tweak rates going forward, in order to "dial in" on appropriate rates as it learned about its skills and pay system.
The implementation process
We had already done some engagement with the field staff about how we were looking at developing an algorithm based on their feelings about pay. They had also already been acculturated to the Developmental Pathways model, which allowed them to progress to cumulative specialisations, so we knew that we weren't likely to unsettle anyone by trying the project.
Gemma, our statistician, went away and did a preliminary build. We provided several case study staff, matched against their existing competencies, and she modelled a line of best fit from their current wages, in order to assign a dollar value to each competency. At a review session we were able to play with the tool, and the project team could look at these values and decide whether they were what the company wanted to be paying (e.g. "is that too low for a team lead?").
From this session, several bugs were ironed out, and pay values tweaked. At the next team breakfast, each staff member was given their own sheet outlining what their pay was made up of, and what the next "step" in their progression to higher pay was.
The algorithm v1.2 is now in place, and staff retention continues to climb. This isn't JUST because of the algorithm, but it is largely because of what the algorithm represents - a new culture of listening, a commitment to fairness, and clarity of future earning opportunities based on skill AND attitude.
If you have any questions about this or any of our other work, please don't hesitate to get in contact. We're happy to share!