Training and employee productivity: Does the relationship vary with regulation? An empirical analysis of the microfinance sector in Bangladesh
Bibliographic record
Abstract
Training and productivity have been found positively correlated in theory and practice. The pattern of the relationship, however, was not explored in pre - and post-regulation in the conceived sector. This paper aims to observe the effects of regulation and training expenses along with other covariates on employee productivity in the Microfinance sector of Bangladesh. Using annual data of MRA-licensed MFIs, we have estimated both panel and cross-sectional regression models. The regression results confirm the theoretical relationship between training expenses and employee productivity. The regulation also worked positively in enhancing employee productivity. However, in the early stage of regulation, the average productivity gain due to regulation was substantial and was showing an increasing trend but then it declined and reached a constant level of about 4% - 5% each year. Between 2008 and 2011, both regulation and training positively contributed to the gains in average productivity of the employees. After 2012, there was a positive trend of average productivity elasticity of training expenses but there was a flat effect of regulation after 2012. Regulation was found to short-run shifter in the average productivity of employees while training expenses had a positive trend effect.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| 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".