State Ownership and Target Setting: Evidence from Publicly Listed Companies in China*
Bibliographic record
Abstract
ABSTRACT Prior research has examined target setting in market‐driven companies but has not examined target setting in state‐run companies that also have social and political objectives. I examine how Chinese state‐owned enterprises (SOEs) set and revise performance targets to motivate a balanced effort allocation. Using data on financial performance (sales) targets set by SOEs and non‐SOEs during the period 2006–2016, I predict and find that the financial targets of SOEs are easier to achieve than those of non‐SOEs, and that SOEs with easier financial targets perform better regarding corporate social responsibility. I also predict and find that SOEs ratchet financial targets upward less than non‐SOEs to keep them easy to achieve and, as a consequence, SOE managers are less likely to game performance to avoid future target increases. The results are robust to alternative measures of state influence and alternative measures of financial targets. These findings suggest that firms balance their multiple objectives through strategically setting and revising financial targets. In doing so, this study provides a better understanding of target‐setting practices in organizations pursuing multiple, sometimes conflicting, objectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".