Empirical Analysis of Efficiency of Community Banks in Tanzania
Bibliographic record
Abstract
Efficient utilization of resources is critical for effective performance of banks. This paper measured efficiency of community banks in Tanzania and compares performance between two community banks’ categories during 2002-2014. Efficiency was estimated using Data Envelopment Analysis (DEA) approach while the independent samples t-test was applied on means of efficiency to compare performance between the banks’ categories. The paper establishes that, most community banks were inefficient, suggesting that there was effect of unnecessary additional costs on the banks performance. Despite the general poor performance, community banks in Tanzania were generally operating at a decreasing part of the average cost curve, which granted an opportunity for expansion and exploitation of scale economies. The paper further establishes that efficiency of respective categories of community banks did not differ significantly; implying that the bank type did not matter as regards efficiency issue. The policy implications are; community banks should effectively manage their cost structure in order to improve performance, and a separate regulatory framework should be applied to community banks to take care of their uniqueness.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".