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Record W2913147232 · doi:10.1257/aeri.20180358

Estimating US Consumer Gains from Chinese Imports

2019· article· en· W2913147232 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAmerican Economic Review Insights · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of CanadaBooth School of Business, University of ChicagoUniversity of Chicago
KeywordsEconomicsInflation (cosmology)BenchmarkingPrice indexConsumer price index (South Africa)Point (geometry)Percentage pointConstruct (python library)Index (typography)Monetary economicsInternational economicsEconometricsMonetary policy

Abstract

fetched live from OpenAlex

We estimate the size of US consumer gains from Chinese imports during 2004–2015. Using barcode-level price and expenditure data, we construct inflation rates under CES preferences, and use Chinese exports to Europe as an instrument. We find significant negative effects of Chinese imports on US prices. This effect is driven by both changes in the prices of existing goods and the entry of new goods, and it is similar across consumer groups by income or region. A simple benchmarking exercise suggests that Chinese imports led to a 0.19 percentage point annual reduction in the price index for consumer tradables. (JEL E21, E31, F14, P33)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.811
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.024

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.251
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it