China's Prosperous Middle Class and Consumption-led Economic Growth: Lessons from Household Survey Data
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".