Technical Efficiency of Malaysia’s Development Financial Institutions: Application of Two-Stage DEA Analysis
Bibliographic record
Abstract
This paper investigates the technical, pure technical, and scale efficiency of 9 development financial institutions (DFIs) operating in Malaysia from 2006-2012 and factors affecting the efficiency of development financial institutions, using the two-stage data envelopment analysis (DEA). Results revealed that the mean technical efficiency of DFIs in Malaysia is 78 percent. Two banks namely BPMB and SCC are the benchmark banks identified by DEA scores. Results show that the role of scale inefficiency in overall technical inefficiency is comparatively less than managerial inefficiency. Results also show that only BPMB, SCC experienced constant returns to scale for the period 2006-2012, fulfilling their primary objective of contributing towards the socio-economy development of the state. BSN, a major saving institution, experienced decreasing returns to scale in 2009 and 2012. SME bank, whose mission is to develop SMEs, too experienced decreasing returns to scale during 2009-2010. CGC and Agro bank also experienced decreasing returns to scale in 2008-2009 and 2010-2012. In second stage, results of the OLS regression analysis provides that Loans to total assets, natural logarithm of total assets, Loan-Loss provision to total loans, non-interest income to total assets, return on assets and total shareholders’ equity to total assets are related to technical efficiency but loans to total assets, positively related to technical efficiency and significant and shows that banks with higher loan to asset ratios tend to have higher technical efficiency scores; non-interest income to total assets is negatively related to technical efficiency and significant revealing that development financial institutions which derive a higher proportion of income from non-interest sources tend to report lower efficiency scores. Return on assets are found significant in explaining the Malaysian development financial institutions efficiency from 2006-2012.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".