Institutional varieties, governance quality, and firm‐level innovation in emerging economies: Case of India
Bibliographic record
Abstract
Abstract This study examines how institutional varieties at the subnational (state) level influence firm‐level innovation in an emerging economy—India. Knowledge of how institutional varieties influence firm‐level innovation is derived principally from country‐level studies involving multiple developed countries. Research on emerging economies is sparse and tends to follow country‐level approaches involving multiple countries. Research involving a single emerging economy where there are substantial institutional varieties between regions is thin. The institutional varieties of some emerging countries are so striking that they can be viewed as several countries within a country, for example, India, China. This study contributes to the innovation literature on the role of institutional varieties on firm‐level innovation by focusing on a different level of analysis—a single, emerging economy with substantial institutional varieties across the different states of India. Innovation in emerging economies is a topic of increasing academic interest. A multilevel study involving regional‐ and firm‐level factors is employed. Firm‐level data are from the World Bank Enterprise Survey and regional‐level data are from statistical agencies in India. The results confirm that institutional varieties have major impacts on firm‐level innovation. The research, policy, and managerial implications are discussed.
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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 | 0.000 | 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 | 0.001 |
| 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".