Growth and Capital Structure of the Listed Chinese Real Estate Firms: An Empirical Investigation
Bibliographic record
Abstract
The Chinese economy has posted an average annual growth rate of around 10% for at least a decade now. The boom in real estate is one of the most visible symbols of China’s steps to become an economic and political giant in this century. This paper examines empirically the determinants of capital structure of all the listed real estate companies in China between 1992 and 2001. We find evidence that is remarkably consistent with Myers (1977) growth options arguments. The listed real estate companies are large, mature, and formerly state-owned enterprises in which the State still maintains a large and illiquid ownership position. Yet, evidence for the Myers model on them is stronger than that on the listed entrepreneurial private firms, which are young and without direct state ownership. The macroeconomic boom, especially in real estate development, is offering the listed real estate firms significantly valuable growth opportunities in China. (A Note to the Members of the Conference Committee: The submitted version of our paper covers the period of 1992-2001.) We have just completed data collection for the period of 2002-2009 and are in the process of incorporating new results from this period into our paper. Thus, reported results and discussions in this version are preliminary though hold for the period of 1992-2001. The updated version will still report the current results under a sub-period analysis. We are totally confident that we will have a complete paper for the entire period of 1992-2009 within a few months.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".