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Record W4387504688 · doi:10.1017/s0305741023001261

China's Prosperous Middle Class and Consumption-led Economic Growth: Lessons from Household Survey Data

2023· article· en· W4387504688 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.

Bibliographic record

VenueThe China Quarterly · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsWestern University
Fundersnot available
KeywordsConsumption (sociology)Middle classChinaConsumption functionEconomicsMacroDemographic economicsAggregate dataGeographyMacroeconomicsMarket economyStatisticsProduction (economics)

Abstract

fetched live from OpenAlex

Abstract Can the expansion of a prosperous middle class help China to rebalance to consumption-led growth? We address this question through analysis of macro- and micro-level data. Using macro statistics, we examine trends in national aggregate consumption and GDP growth from 2000 through 2019. We observe growth in aggregate consumption but do not find convincing evidence of consumption-led growth. Using micro-level household survey data from 2002, 2007, 2013 and 2018, we estimate the size of China's prosperous middle class and its contribution to aggregate consumption growth. We find that the prosperous middle class expanded rapidly but contributed less to aggregate consumption growth than expected. We discuss features of this class that diminished its contribution to consumption-led growth, including its low propensity to consume out of income and its limited expansion beyond urban subgroups. We conclude that the expansion of the prosperous middle class is necessary but not sufficient to bring about rebalancing.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.132
GPT teacher head0.334
Teacher spread0.202 · 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