Open Lanes: a transparent, lane-based salary framework

Open Lanes: a transparent, lane-based salary framework

One formula decides every salary: lane × experience × location. Raises happen automatically every year. Nobody negotiates.

Is this pattern for you?

Choose it if you want salaries to be explainable, predictable, and equal for equal work — and you're willing to give up individual pay deals to get that.
Skip it if you want compensation as a management lever: individual negotiation, performance bonuses, or manager discretion. This framework deliberately removes all three. Half-adopting it (a formula plus "exceptions") gives you the overhead without the fairness.
Good fit signals:
  • You want to close pay gaps driven by who negotiates hardest
  • Your team is distributed across cities or countries
  • You want to predict personnel costs years ahead
  • You run a self-managing or high-transparency culture

The problem

Most nonprofits set salaries case by case. That rewards negotiation skill over contribution, creates pay gaps nobody chose (gender gaps included), and turns every hire and raise into a bespoke discussion. Nobody can explain why two colleagues earn what they earn, so nobody trusts the numbers — including the people setting them.

The pattern

One formula: salary = lane base × experience curve × location factor, with two automatic raises per year.
Lanes. Group all roles into 3–5 lanes based on the nature of the work — scope and impact, not job titles, seniority labels, or performance. The test: every team member should be able to spot their own lane from the descriptions alone. An example four-lane structure:
    Strategic direction — sets organizational direction, accountable for multi-year outcomes, represents the organization externally
    Program ownership — owns programs end to end, manages complex stakeholders, coordinates cross-team work
    Skilled execution — leads campaigns, operations, or community work; produces core products like content, events, and policies; brings specialist expertise. Most of a typical team sits here.
    Support & coordination — enables operations; administrative and transactional work
Each lane spans roughly 30 years of experience, with its own start and end salary. No sub-lanes, no "junior/senior" versions of the same lane — those reopen the door to subjective judgment and negotiation. If a role fundamentally changes, the person changes lanes; otherwise experience drives growth.
Experience curve. Within a lane, salary grows with years of relevant experience along a curve: fast in the first 10 years, flattening after 20. This matches real learning curves. On hiring, rate relevant experience with a simple convention: direct experience counts 1:1, adjacent experience 0.5:1, other work experience 0.1:1. This initial rating is the only moment where any discussion takes place.
Two automatic raises per year. A curve raise (lane progression) plus an inflation adjustment (national CPI, fixed once per year on 1 January). Nobody asks, nobody negotiates — the raises simply happen.
Location factor. For distributed teams, adjust for equal net purchasing power using the average of two public cost-of-living indices (Expatistan and Numbeo), anchored to one baseline city. Mind the tax gap: these indices compare net-to-net, but you pay gross salaries. Where the local tax wedge differs from your baseline country, gross up: gross = CoL salary × (1 − baseline wedge) ÷ (1 − local wedge). Fix exchange rates once per year, same as inflation, so every offer within a year is on equal footing.
Freelance conversion. Anchor contractor rates to the same model so freelancing never becomes a backdoor around the no-negotiation principle: gross monthly salary ÷ 100 for the hourly rate. That bakes in roughly 1.31× employer costs, ~210 billable days per year, and a buffer for freelance risks.
The decision test: can any team member compute a colleague's salary from the published model and get it right? If not, it's not transparent yet.

What to do

    Define the lanes first. This is the hardest part and where legitimacy lives or dies. Test descriptions against your real people before launch: if someone can't confidently place themselves, rewrite.
    Set anchors and curve. Benchmark lane start and end points against your sector CAO, nonprofit salary surveys, and local vacancy data.
    Place everyone. A workable transition assumption: current salaries roughly reflect relevant experience, since everyone accepted them for their role. Correct obvious imbalances upward only — never cut salaries at introduction.
    Model the budget 3 years out. Curve raises plus inflation compound; know your personnel cost growth before you commit.
    Assign one role accountable for the framework, lane placements, initial experience ratings, and offer consistency (a Compensation & Benefits role). Without a single owner, pay drifts back to case-by-case.
    Publish the model and launch "safe enough to try." Announce a review after year one. If you ever need to change the framework, say so publicly in advance and explain why.

What's mandatory regardless

Statutory minimum wage and any CAO you're bound to set the floor. The EU Pay Transparency Directive (in force for member states since June 2026) requires employers to explain pay levels and criteria on request — a published formula satisfies this by construction, which is one more reason to like it.

Your variation

Record your organization's choices:
  • Number of lanes, their names and descriptions
  • Anchor amounts per lane and the curve you use
  • Baseline city and which locations get a multiplier
  • Share of salary that is cost-of-living adjusted: 100% (equal purchasing power) or 50% (dampens extremes; Giving What We Can's approach). Both are defensible — pick one and write down why.
  • Tax wedge source (use your payroll provider's effective rates, don't estimate)
  • Inflation index and fixing date
  • Transparency level: amounts public, or formula public with amounts on request
  • Whether the freelance conversion applies, and to which engagements

With thanks

This pattern stands on the shoulders of two Dutch self-organized companies that built and published transparent salary models years before it was fashionable: Viisi, whose lane-plus-curve model — tested for years in Amsterdam's competitive financial market — is the direction we recommend, and Voys, whose more data-driven model taught the field a lot about where complexity creates overhead. Special thanks to Marc-Peter Pijper (Viisi) and Rosien van Toor (Voys) for generously sharing how their models work in practice.