Bibliographic record
Abstract
I study the relationship between management quality and innovation input, and output of firms in ten emerging countries using data from the Management, Organization and Innovation (MOI) Survey. I find that management quality is tightly connected to the decisions of firms to invest in R&D. An improvement in management quality from the 25th percentile to the median is associated with a 3.3 percentage point increase in the propensity to invest in R&D. Furthermore, there are positive but weak association between management quality and product innovation. The empirical results for individual management practices show that the quality of incentive management is intimately connected to innovation performance. The quality of monitoring management is related to higher inputs into innovation, but not to innovation output. The quality of incentive management is related to higher input into innovation, but not to innovation output. All results hold after controlling for differences in management quality by industries. Additional analysis of management quality asymmetry shows that the results are driven mainly by firms with low quality management.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".