STATE-OWNED ENTERPRISE BEHAVIOURAL RESPONSES TO TRADE REFORMS: SOME ANALYTICS AND NUMERICAL SIMULATION RESULTS USING CHINESE DATA
Bibliographic record
Abstract
AbstractWe note the absence of prior literature on analytical structures to be used for China and other economies with extensive SOEs when evaluating behavioural responses of SOEs to trade policy and other changes. This is despite both the large empirical literature discussing the productivity effects of Chinese SOE enterprise reform, and wider policy discussion of the potential impacts of various reform initiatives. We present two simple analytical formulations of SOE behaviour in response to trade policy change with the aim of investigating how traditional competitive models of enterprise behaviour can mislead when used in policy debate. One formulation centres on SOE managerial control. In this enterprise managers are politically appointed, expect any non-performing loans to be recapitalized by state banks and hence capital is centrally allocated by credit rationing. The managers are assured to maximize the size of the enterprise rather than profits since this yields maximal networking benefits to managers. This implies labour is priced at its average rather than its marginal product, and with a competitive non-manufacturing (agricultural) industry free trade is not optimal policy. The other assumes worker control of SOEs and that workers satisfice in their supply of effort to the enterprise given both fixed wage rates and enterprise employment and otherwise shirk or pursue second jobs. In this formulation the enterprise meets their budget constraint and covers costs. With leisure in the preferences of enterprise members, their leisure consumption will be implied by the satisfying behaviour of the enterprise and will be non-optimal. In both model variants, implications for trade policy are different from those of a standard competitive model, and computations using models calibrated to 2003 Chinese data suggest the differences can be large.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".