IESET.
Hypotheses·trade·trade_lib_us_china_phase_one_2020

The US-China Phase One Trade Agreement (signed 2020-01-15) committed China to USD 200bn of additional US imports over 2020- 2021.

This commitment was not met: actual purchases reached only ~58% of target according to PIIE tracking through 2021. The descriptive test is whether US-China bilateral merchandise trade shows the agreement's anticipated step-up against pre-agreement trend.

PARTIALengine/runs/trade_lib_us_china_phase_one_2020

PARTIAL — shape=bilateral, |Δ_log|=0.589, ratio=0.555; threshold 58.0%, observed 58.9%; claim direction ambiguous

confidence cueThe result is useful, but not decisive. Treat it as a clue, not a settled conclusion.

policy briefMixed or noisy

In ordinary language

When countries open more of the economy to trade and competition, do people end up with better long-run income or productivity outcomes?

plain answer

The evidence is suggestive but not decisive. shape=bilateral, |Δ_log|=0.589, ratio=0.555; threshold 58.0%, observed 58.9%; claim direction ambiguous

why it matters

This matters because trade claims should change belief only when they survive a pre-declared empirical test.

how the test works

It compares 2 country or place units from 2015 to 2024, using a descriptive design.

what was measured
What we checked
  • Us merchandise exports pct income
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

No evidence packet has been generated yet.

Results

engine/runs/trade_lib_us_china_phase_one_2020
1007550250201520202024USACHN
illustrative sketch · run pending
No coefficients yet. When the model fires, this chart will show us_merchandise_exports_pct_gdp across 2 sampled countries over 2015–2024.
The shapes above are stylised — none of the lines are real data.
Placeholder for trade_lib_us_china_phase_one_2020. Published chart will be generated from engine/runs/trade_lib_us_china_phase_one_2020/chart_data.json.

Pre-registration

registration ordering unverified
first-spec commit 098ce96 · 2026-04-30T12:57:33Z
run generated · 2026-04-30T08:07:40Z
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.

The US-China Phase One Trade Agreement (signed 2020-01-15) committed China to USD 200bn of additional US imports over 2020- 2021. This commitment was not met: actual purchases reached only ~58% of target according to PIIE tracking through 2021. The descriptive test is whether US-China bilateral merchandise trade shows the agreement's anticipated step-up against pre-agreement trend.

Falsification criterion — what would disprove this

set before the run · honoured after

This hypothesis is considered falsified if:

SUPPORTED (null on commitment fulfilment) if US merchandise- exports-to-GDP did not show a step-up materially exceeding the 2015-2019 pre-agreement trend over 2020-2021. REFUTED if aggregate exports rose markedly above trend in 2020-2021 (which would be inconsistent with the documented under- fulfilment).

formal test & threshold
test:      descriptive_us_exports_phase_one_step_up
threshold: PRIMARY: US exports/GDP 2020-2021 mean does not exceed 2015-2019 linear trend extrapolation by more than +1 pp.

Method

Template
descriptive
Clustering
none
Sample
2 countries · 2015 – 2024
Evidence type
descriptive

Descriptive evaluation. Aggregate WDI data cannot isolate the US-China bilateral; the spec records the aggregate trajectory and discloses the qualitative finding from the bilateral tracking literature.

Data

VariableSourceTransform
us_merchandise_exports_pct_gdp
outcome
world_bank_wdi:NE.EXP.GNFS.ZStier 2
level

● ready  · ● pending  · ● reconstruct-needed

Strongest opposing argument

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

Authored framework. Read the transparency note.