Bibliographic record
Abstract
This dissertation consists of three essays that study the industrial organization of China's manufacturing sector from an empirical perspective. It uses structural estimation to look into the performance of China's manufacturing sector with a particular emphasis on the steel industry - a key sector in China that produces half of the world's steel. This dissertation also examines the financial constraints that manufacturing firms face. Chapter 1 documents the development of the steel industry in the past two decades. Chapter 2 studies productivity differences in vertically-integrated Chinese steel facilities, using a unique dataset that provides equipment-level information on inputs and output in physical units for each of the three main stages in the steel value chain, i.e., sintering, iron-making and steel making. We find that private integrated facilities are more productive than provincial state-owned facilities, followed by central state-owned facilities. This ranking lines up with our productivity estimates in the two downstream production stages, but central state-owned facilities outperform in sintering, most likely because of their superior access to high-quality raw materials. The productivity differential favoring private facilities declines with the size of integrated facilities, turning negative for facilities larger than the median. We attribute this pattern to differences in the internal configuration of integrated facilities, which reflect the greater constraints confronting expanding private facilities. Increasing returns to scale within each stage of production partially offset these costs, and rationalize the choice of larger facilities. Chapter 3 draws on the Chinese Industrial Survey Data from 1998 to 2007 to examine financing constraints in the manufacturing sector. Building on the Euler Equation approach and applying the dynamic GMM estimation, we find that on average private firms face more obstacles in accessing credit than state-owned enterprises (SOEs). Contrary to the widely accepted view that China's private sector is largely excluded from formal credit allocation, we find that large firms, both state-owned and private, are not credit constrained. Medium and small SOEs are financially constrained, although to an extent less than their private counterparts of similar size. Moreover, the capabilities of firms in accessing external finance differ by economic region and across industries.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".