# Stage 130 — empirical calibration source-quality gate

The first empirical calibration work package checks nine rounded Israeli corporate tax receipts/GDP observations from mixed OECD editions. It reports descriptive levels and leave-one-observation-out sensitivity, preserving source URLs and publication vintages.

**This is not a calibrated macroeconomic model.** Tax revenue as a share of GDP cannot identify an effective corporate tax rate, tax base, productivity elasticity, price pass-through or redistribution capacity. No structural parameters are updated. Missing years and mixed editions prevent treating the observations as a harmonized time series.

The next empirical work requires verified, aligned official annual GDP, tax-base, employment, price and productivity series, plus a prespecified identification strategy. Do not fill gaps by interpolation and present them as observed.

Reproduce: `python scripts/empirical_calibration_gate_v130.py --output data/processed/empirical_calibration_gate_v130.json`.

## Verified findings — GitHub Actions run 413

Nine rounded, sourced Israeli corporate tax receipts/GDP observations were accepted. Descriptive mean: **3.233333%**; median **3.2%**; minimum **2.6%**; maximum **4.3%**. The leave-one-observation-out mean ranges from **3.1%** to **3.3125%**.

These are descriptive statistics for a sparse, mixed-vintage dataset, not calibrated structural parameters. The validation gate explicitly reports `calibrated=false`. No simulator parameters were modified. An independent official series and a defensible identification strategy remain prerequisites for estimating the effects of automation on labor, prices, corporate tax bases and transfers.