Monetary and Fiscal Stimuli, Ownership Structure, and China's Housing Market
Bibliographic record
Abstract
In the recent financial crisis, macroeconomic stimuli produced mixed results across developed economies. In contrast, China's stimulus boosted real GDP growth from an annualized 6.2% in the first quarter of 2009 trough to 11.9% in the first quarter of 2010. Amidst this phenomenal response, land auction and house prices in major cities soared. We argue that the speed and efficacy of China's stimulus derives from state control over its banking system and corporate sector. Beijing ordered state-owned banks to lend, and they lent. Beijing ordered centrally-controlled state-owned enterprises (SOEs) to invest, and they invested. However, our data show that much of this investment was highly leveraged purchases of real estate. Residential land auction prices in eight major cities rose about 100% in 2009, controlling for quality variation. Moreover, higher price rises occur these SOEs are more active buyers. We argue that these centrally-controlled SOEs overbid substantially, fueling a real estate bubble; and that China's seemingly highly effective macroeconomic stimulus package may well have induced costly resource misallocation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".