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# Phase 4 — Structural change, total monthly series (corpus v2, month-precision records, 2018-02 → 2026-06)

Source: results/econometrics/its_results.json, fit_total.csv, placebo_total.csv (script econometrics/run_structural_change.py). Interpretation pending PI checkpoint H-4. The 2022-11-30 date is a reference point; nothing below is a causal estimate.

# Interrupted time series at 2022-12 (log counts, month-of-year dummies, Newey–West 6 lags)

Quantity Estimate
Pre-period monthly growth (slope) +0.0031 log points/month (SE 0.0005) ≈ 0.31 %/month
Level change at 2022-12 -0.009 (SE 0.024, t = -0.40, p = 0.69)
Slope change after 2022-12 +0.0108/month (SE 0.0009, t = 12.1, p = 6.4e-34) → post-period growth ≈ 1.39 %/month
Placebo (all 65 candidate months) level: rank 63/65, p = 0.97; slope: rank 1/65, p = 0.015
Placebo (baseline months only, n = 34) level p = 1.0; slope p = 0.0
Donut (±3 months excluded) level +0.049 (p = 0.056); slope +0.0110 (p = 6.7e-24)
R² 0.966 (n = 101)

# Alternative event dates (same model)

Event Level change Slope change
chatgpt (2022-12-01) -0.009 (p = 0.690) +0.0108 (p = 6.42e-34)
gpt3 (2020-06-01) -0.091 (p = 0.122) +0.0059 (p = 8.44e-05)
dalle2 (2022-05-01) -0.075 (p = 0.006) +0.0100 (p = 1.22e-28)
gpt4 (2023-04-01) +0.025 (p = 0.305) +0.0109 (p = 6.68e-30)
o1 (2024-10-01) +0.103 (p = 0.008) +0.0115 (p = 2.91e-10)

# Unknown-break methods (seasonally adjusted log series)

  • Bai–Perron (BIC and LWZ agree): break date(s) ['2022-04-01'] ; BIC by m = {'0': -463.8, '1': -555.25, '2': -551.53, '3': -545.89}
  • PELT: ['2023-05', '2024-12']
  • BOCPD MAP run-length resets: []
  • Sensitivity to the last complete month: {k: (round(v['its_chatgpt']['level_change'],3), round(v['its_chatgpt']['slope_change'],4), v['bai_perron']) for k, v in r['sensitivity_last_complete_total'].items()}

# Reading (preliminary, H-4 pending)

The total output series shows no discrete level shift at the ChatGPT release month but a marked and robust acceleration of the growth rate (from ≈ 0.3 % to ≈ 1.4 % per month), which the unknown-break estimators date to spring 2022, i.e. before November 2022. The acceleration therefore predates ChatGPT in the aggregate series; category-level series (after classification) will show where it is concentrated. Caveats: right-truncation (series cut at 2026-06), OpenAlex coverage growth, and the exclusion of year-only dated records.

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