Evidence-Based
Real studies, named sources, verifiable citations. We collect the published evidence on ATS systems, hiring bias, resume effectiveness, and the labor market — so you don't have to take folk wisdom on faith.
How many resumes really get rejected before a human sees them, who's running the systems, and what the published data does — and doesn't — say.
Bertrand-Mullainathan, the 2023 NBER replication, age discrimination audits, and what the field-experiment literature actually shows about hiring bias.
The famous "6 seconds" claim, where it comes from, what the eye-tracking data actually measured, and how to write a resume for the way recruiters really read.
Survey data on how candidates use AI to write resumes, how often employers can tell, and what — if anything — they do about it.
How much hiring is actually remote in 2026, where the equilibrium is settling, and what the major labor-market datasets show about the post-pandemic normalisation.
IBM, Google, and 50+ companies announced commitments to skills-first hiring. The published data on what actually changed is more modest than the headlines.
State pay transparency laws moved fast between 2021 and 2025. Here's what the data shows about applications, posted ranges, and wage gaps.
How many applications per requisition, how long the median hiring process takes, and where the bottlenecks are — with data from SHRM, LinkedIn, and Greenhouse.
Where the one-page rule came from, what the ResumeGo field experiment and recruiter surveys actually found, and when a second page wins.
What surveys really show about whether recruiters read cover letters, when they tip a decision, and how applicant tracking systems handle them.
Referred applicants are hired and retained at far higher rates than cold applicants — but the same mechanism reproduces the existing workforce's demographics. What the published evidence shows.
Survey evidence on candidates ghosting employers and employers ghosting candidates: how common each is, the year-over-year trend, and what the data does and doesn't establish.
The field-experiment evidence on age discrimination in hiring, which resume signals reveal age, and how large the documented callback gaps actually are.
Field experiments on the resume-gap penalty, ATS gap filters, and survey data on shifting attitudes — separating what employers say from what they do.
What the laws actually require of AI resume screeners and video interviews — NYC's bias-audit law, Illinois, Colorado, the EU AI Act, and EEOC enforcement — and how thin compliance still is.
Who negotiates, who gets penalized for it, and how much it explains the pay gap — from the classic "women don't ask" data to field experiments and newer studies showing the picture has shifted.
External hires get paid more but perform worse early and quit more often. The research on internal moves, retention, and why employers keep hiring outside anyway.
A field experiment found comprehensive LinkedIn profiles lifted callbacks 71%, but a bare profile didn't help. What the data shows about social profiles, screening, and its limits.
Every claim on these pages is sourced. We cite the primary research where possible — peer-reviewed papers, NBER working papers, government statistics, and industry benchmark reports with published methodology.
Where a statistic has propagated through many secondary sources, we trace it back to its original publication and flag the source date. If a number is older than five years, we say so rather than implying currency. If we can't verify a source, we drop the claim — we don't fabricate citations.
Findings are not opinions. They're aggregations of what the published evidence actually says. Where the evidence is contested or thin, we say that too.
Build a resume that benefits from what the data actually shows about hiring.