Do SME Policy Improve Firm Productivity? Empirical Evidence from Latin America and China
Bibliographic record
Abstract
Based on World Bank Enterprise Survey dataset, this paper uses instrumental variables regression to examine whether Small and Medium Enterprises (SMEs) are more efficient and if SMEs policies (SME Policy) have any influence on firm-level Total Factor Productivity (TFP) in two important emerging economies: Latin America and the Caribbean (LAC) and China. The results show that, first, there is a positive correlation between firm size and TFP in LAC but not in China. Second, training, line of credit/loan for SMEs are proved to have a significant positive effect on firm TFP. Specifically, for Chinese firms, the training programs are most effective while for LAC firms, loan and credit are most effective. Third, R&D related innovation improves the efficiency, especially for Chinese firms. Fourth, government shareholding improves efficiency in China since they may provide some help in acquiring loans, while in LAC countries, the government shareholding has a negative effect. Based on above mentioned results, I suggest that Latin American countries should set financial relaxing as the primary goal of SME policies and China should provide more training programs and improve incentive mechanism in technology innovation for SMEs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".