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Págame
Methodology

How Págame computes your number.

Your market value is deterministic, gender-blind, and auditable — never an AI guess. This page shows the full path from your résumé to your target, and the sources behind every step.

Engine target-1.1.0·Bias-tested every deploy·Data: BLS OEWS
The pipeline
Résumé
You upload; Págame reads
Pay Profile
You verify every field
Market data
Government wage curves
Range
Your market-value band
Target
Level-adjusted, auditable
Script
Never below your target

The VALUE framework

Five stages. Each one is inspectable — you can see what went in, what came out, and why.

V
Verify
Págame reads your résumé and you confirm what it found — role, level, scope, geography, industry. Nothing enters the computation without your sign-off. Gender never enters at all: there's nowhere to put it.
A
Analyze
Your verified profile is matched to the government's occupational wage data — BLS OEWS wage curves for your role and metro, cross-checked against O*NET skill demand and Census industry data.
L
Locate
Your current compensation is placed on the wage curve — your percentile, your distance from the median, and your market-value range, straight off the curve.
U
Understand
Every factor behind your result is named — role, level, location, industry — with a confidence read and the sources cited, every time. If confidence is low, Págame says so.
E
Execute
Your target becomes a plan: evidence ranked by strength, a word-for-word script anchored to your number, and — when it's over — an outcome you record to sharpen the next computation.
The target, bridged

One equation. Every part sourced.

Market median
$156,000
Product managers, Chicago
BLS OEWS · May 2025 estimates
×
Level adjustment
+12%
Senior level, expanded scope
Engine target-1.1.0 · deterministic
=
Your target
$174,700
Which is why a senior target can sit
above the band’s high.
The market may contain inequality. Gender is never an input — Págame can’t lower your estimated value because of it — and because fairness needs proof, we test every release for disparate outcomes and publish the results.

What confidence means here.

High
Three or more corroborating sources; your role maps cleanly to an occupational code; recent data for your metro.
Medium
A clean role match but thinner local data — the range widens, and Págame tells you which factor is uncertain.
Low
A hybrid or emerging role. Págame shows the nearest anchors and never presents a low-confidence number as a fact.
Págame Power Score™

Págame tells you two different things, and the gap between them is where negotiations are lost. Your target is what to ask for. Your Power Score is whether you’re in a position to insist on it — and what to build first if you aren’t. Knowing you’re underpaid and being ready to fix it are not the same thing: you can have an airtight case and still ask too early, too quietly, with nothing to point at.

Every factor in it is yours to change, most of them inside a week — so a low score is a to-do list ranked by what each item is worth, never a verdict on you. Leverage is built.

It is computed by the same rules as your number: a fixed, versioned formula, never AI, never your gender. It weighs three named factors — your pay position, your outside evidence (a live offer or an active search), and the strengths you’ve named. The confidence of the data stays where it belongs — shown with your number, never scored against you: how well the government sampled your metro is a fact about the data, not about your position. We publish the categories, not the internal weights; every score is stamped with the version that produced it.

Cost of Staying™

Fully disclosed arithmetic: your yearly gap held constant over one, three, and five years. No growth assumptions, no forecast — a floor, because if your pay and the market both grow, the real cost compounds. It only appears when you’re below your market rate; we never invent a cost that isn’t in the data.

Data freshness
BLS OEWS, updated yearly
Methodology version
Engine target-1.1.0
Bias testing
Every deploy, published
Sources
BLS · U.S. Census · O*NET

See the method work on your career.

Private · Evidence-based · Free to start