Learning-by-doing effect: Evidence from firms of an emerging economy
Bibliographic record
Abstract
In the learning theory, the vast majority of both theoretical and applied research have concentrated on the developed world.In contrast, developing, in particular, emerging economies have drawn much less attention.Moreover, empirical outcomes are conflicting, with some studies revealing learning-bydoing to have a positive impact, but others finding learning-by-doing to have a negative effect on the performance.Therefore, this study is conducted through the Metropolis-Hastings and Gibbs samplers in the context of a Cobb-Douglas specification to evaluate the effects of learning-by-doing on firm performance on a panel data of the 227 manufacturing firms listed on the Vietnamese stock market.A Bayesian mixed-effects regression used allows for capturing the varying effects of all the researched firms.The study found that firm-specific learning-by-doing has a strong positive influence on firm performance.This finding is accordant with the predictions of the learning theory, many previous investigations, as well as the fact that in a fast-growing economy like Vietnam, firm-specific learningby-doing is closely associated with economic growth.Some helpful policy implications proposed are aimed at increasing productivity for firms in emerging economies.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".