IESET.
Hypotheses·growth·great_leap_forward_famine_output_collapse_1959_1961

Mao Zedong's Great Leap Forward (1958-1962), characterised by forced collectivisation into People's Communes, Lysenkoist rejection of scientific agronomy, diversion of rural labour to backyard steel production, and cadre-competition-driven inflation of reported harvests, produced a canonical institutional-economic collapse that manifests as >=7 of 10 pre-registered extreme-outcome metrics, each drawn from an independent data source or methodology family and measuring a different causal layer (demographic mortality, agricultural output, macroeconomic contraction, institutional coverage, human capital).

The canonical-case claim is that no non-war peacetime economy in the 20th-century panel matches even 4 of these 10 thresholds simultaneously; China 1958-1962 matches most. A refutation (<=3 metrics met) would indicate that the consensus demographic and output reconstructions (Banister 1987, Ashton et al. 1984, Peng 1987, Yang Jisheng 2008) are substantially overstated, or that the institutional-quality coding of the GLF is misplaced.

INCONCLUSIVEengine/runs/great_leap_forward_famine_output_collapse_1959_1961

INCONCLUSIVE_DATA_PENDING — no outcome variable loaded; missing: ['derived: count of canonical_metrics with threshold met']

confidence cueResult card produced; verdict unclassified.

policy briefCoverage too thin

In ordinary language

Over a long period, do more market-oriented institutions translate into higher income or productivity, once the comparison looks beyond a single success story?

plain answer

This test cannot make a firm call yet. no outcome variable loaded; missing: ['derived: count of canonical_metrics with threshold met']

why it matters

Growth claims can look convincing in single success stories. This test asks whether the pattern survives a broader comparison.

how the test works

It compares 1 country or place units from 1958 to 1965, using a multi metric checklist design.

what was measured
What we checked
  • Multi metric collapse score
what this does not prove

A single test is not the whole truth. It narrows the claim under a specific sample, time period, and method. Strong policy conclusions need the pattern to survive nearby tests, alternative data, and serious objections.

verification

11 input datasets, 0 unresolved missing series, provenance status: partial provenance.

Results

engine/runs/great_leap_forward_famine_output_collapse_1959_1961
1007550250195819621965CHN
illustrative sketch · run pending
No coefficients yet. When the model fires, this chart will show multi_metric_collapse_score across 1 sampled countries over 19581965.
The shapes above are stylised — none of the lines are real data.
Placeholder for great_leap_forward_famine_output_collapse_1959_1961. Published chart will be generated from engine/runs/great_leap_forward_famine_output_collapse_1959_1961/chart_data.json.

Who has skin in the game — schools predicting on this

17 schools list this hypothesis as a test of their position. The chips below are school-level scoreboard outcomes, not a second hypothesis verdict.

hypothesis verdict vs scoreboard outcome

The banner verdict judges this hypothesis as written. The scoreboard asks whether each school's polarity-corrected prediction was right. Raw status is not a school win: SUPPORTED supports schools that needed SUPPORTED, but refutes schools that needed REFUTED.

Pre-registration

registration ordering unverified
first-spec commit 4c8ce8e · 2026-07-18T22:11:21Z
run generated · 2026-06-28T22:02:37Z
Run timestamp predates this path's first git-add commit (rebase, rename, or pre-git local run). Spec hash is still the path's first-add commit — not repository HEAD — but ordering is not a clean pre-registration proof.

Mao Zedong's Great Leap Forward (1958-1962), characterised by forced collectivisation into People's Communes, Lysenkoist rejection of scientific agronomy, diversion of rural labour to backyard steel production, and cadre-competition-driven inflation of reported harvests, produced a canonical institutional-economic collapse that manifests as >=7 of 10 pre-registered extreme-outcome metrics, each drawn from an independent data source or methodology family and measuring a different causal layer (demographic mortality, agricultural output, macroeconomic contraction, institutional coverage, human capital). The canonical-case claim is that no non-war peacetime economy in the 20th-century panel matches even 4 of these 10 thresholds simultaneously; China 1958-1962 matches most. A refutation (<=3 metrics met) would indicate that the consensus demographic and output reconstructions (Banister 1987, Ashton et al. 1984, Peng 1987, Yang Jisheng 2008) are substantially overstated, or that the institutional-quality coding of the GLF is misplaced.

Falsification criterion — what would disprove this

set before the run · honoured after

This hypothesis is considered falsified if:

Hypothesis is SUPPORTED if >=7 of 10 canonical metrics meet their pre-registered thresholds. Hypothesis is REFUTED if <=3 of 10 metrics meet their thresholds. The 4-6 metric range is inconclusive. Additional falsification: if any non-war, non-canonical-collapse peacetime country in the 20th-century panel matches >=4 of these 10 thresholds, the metric-set is not GLF-discriminating and the hypothesis is methodologically invalid regardless of the China metric count. The January 1962 readjustment recovery (metric 10) is itself a built-in falsification — if grain output and fertility did not rapidly recover after the policy reversal, the institutional attribution (versus structural / weather) would be falsified.

formal test & threshold
test:      multi_metric_canonical_case_pattern_match
threshold: metrics_met_china_1958_1962 >= 7 AND max_metrics_met_non_war_non_canonical_peer <= 3

Method

Template
multi_metric_checklist
Sample
1 countries · 19581965
Evidence type
canonical_case_multi_metric

For each of the 10 canonical metrics, evaluator fetches the specified source (primary literature reconstructions for the Chinese state-statistics-censored window) and evaluates the threshold. Support if metrics_met >= 7, refute if metrics_met <= 3, inconclusive in the 4-6 range. Benchmark check: no non-war peacetime country in the 20th-century Maddison + UN WPP + FAOSTAT panel should match >=4 of these thresholds; if one does (candidates to check: Soviet famine 1932-1933, North Korean famine 1994-1998, Cambodian famine 1975-1979 — all plausibly non-peer-set-valid because each is itself a canonical institutional-collapse case), the metric-set needs revision to ensure the GLF discrimination is against ordinary peacetime economies, not against other canonical disasters.

Data

VariableSourceTransform
multi_metric_collapse_score
outcome
derived: count of canonical_metrics with threshold metcount

ready  ·  pending  ·  reconstruct-needed

Detailed result card

Result card — great_leap_forward_famine_output_collapse_1959_1961

Verdict: INCONCLUSIVE_DATA_PENDING — no outcome variable loaded; missing: ['derived: count of canonical_metrics with threshold met']

Pre-registration

  • Claim: Mao Zedong's Great Leap Forward (1958-1962), characterised by forced collectivisation into People's Communes, Lysenkoist rejection of scientific agronomy, diversion of rural labour to backyard steel production, and cadre-competition-driven inflation of reported harvests, produced a canonical institutional-economic collapse that manifests as >=7 of 10 pre-registered extreme-outcome metrics, each drawn from an independent data source or methodology family and measuring a different causal layer (demographic mortality, agricultural output, macroeconomic contraction, institutional coverage, human capital). The canonical-case claim is that no non-war peacetime economy in the 20th-century panel matches even 4 of these 10 thresholds simultaneously; China 1958-1962 matches most. A refutation (<=3 metrics met) would indicate that the consensus demographic and output reconstructions (Banister 1987, Ashton et al. 1984, Peng 1987, Yang Jisheng 2008) are substantially overstated, or that the institutional-quality coding of the GLF is misplaced.
  • Falsification rule: Hypothesis is SUPPORTED if >=7 of 10 canonical metrics meet their pre-registered thresholds. Hypothesis is REFUTED if <=3 of 10 metrics meet their thresholds. The 4-6 metric range is inconclusive. Additional falsification: if any non-war, non-canonical-collapse peacetime country in the 20th-century panel matches >=4 of these 10 thresholds, the metric-set is not GLF-discriminating and the hypothesis is methodologically invalid regardless of the China metric count. The January 1962 readjustment recovery (metric 10) is itself a built-in falsification — if grain output and fertility did not rapidly recover after the policy reversal, the institutional attribution (versus structural / weather) would be falsified.
  • Falsification test: multi_metric_canonical_case_pattern_match
  • Event year: (not extracted)

Estimate

  • Error: no outcome variable loaded; missing: ['derived: count of canonical_metrics with threshold met']

Variables resolved

Variables missing data

  • derived: count of canonical_metrics with threshold met (outcome, name=multi_metric_collapse_score)

Generated by scripts/run_event_study.py at 2026-06-28T22:02:37+00:00

Strongest opposing argument

Every hypothesis ships with its charitable opposing argument. The framework earns credibility by handling objections at their strongest, not weakest.

Notes

Methodological rationale: single-estimator causal tests (panel FE, synthetic control, event study) are inappropriate for the GLF because (a) outcome data is materially censored in the exact window where the effect is largest (1958-1962 Chinese state statistics are consensus-unreliable), (b) the treatment is a multi-year bundled institutional complex (collectivisation + Lysenkoism + backyard steel + cadre-pressure informational failure), (c) the donor-pool problem is severe (no peer country has comparable pre-1958 land-reform baseline AND no comparable treatment bundle AND no comparable political-economy shock). In these conditions a pre-registered multi-metric pattern-match is epistemically stronger than a single biased causal estimate. Data-provenance rule: each of the 10 metrics must be evaluable from a different root data source (different publisher OR different methodology family — e.g. Banister cohort back-projection vs Peng provincial reconstruction vs NBS revised physical-output series vs customs archives vs Maddison sectoral aggregate). This is checked by the evaluator to ensure the 10 metrics are not effectively one metric in disguise. Note on provincial-mortality correlation: the Kung & Chen (2011) AER finding that provincial mortality correlates with cadre political pressure (proximity to Maoist faction strongholds) and with collectivisation intensity, net of rainfall anomalies, is preserved in the corpus as a complementary causal test. It is not the primary evidence here because the primary claim of the canonical-case hypothesis is the pattern-match against extreme outcomes, which is logically prior to attribution of the mechanism. Supersession note: this version 1 fully supersedes any prior draft/candidate causal specification of the GLF collapse for framework-validation purposes.

Authored framework. Read the transparency note.