State capitalism, economic systems and the performance of state owned firms
Bibliographic record
Abstract
In this paper, we pursue two related research questions. First, we enquire whether state owned enterprises (SOEs) perform better than privately owned firms in a large variety of emerging markets. To test this, we develop a unique dataset using firm-level data from the World Bank Enterprise Survey (WBES), resulting in a sample of over 50,000 firms from 57 understudied countries including emerging capitalist, former socialist and state capitalist ones. Our results suggest that SOEs do display productivity advantages over private firms in these understudied economies. Our second research question asks whether the performance of state owned firms in these understudied countries is context specific, namely whether performance depends on the institutional system into which a country is classified. We refer to these systems as configurations. In particular, we are interested in whether state owned firms perform better in “state capitalist” countries including China and Vietnam. We find empirical support for the argument that the “state-led” configuration provides better institutional support for the ownership advantages of SOEs than others.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".