Efficiency of Microfinance providers in Pakistan: An Empirical Investigation
Bibliographic record
Abstract
Purpose: The aim of this study is to assess the efficiency of microfinance institutions in Pakistan using quarterly data from microfinance connect of second quarter of 2006 and second quarter of 2016 for comparison of two different time span. To estimate efficiency of microfinance institutions in Pakistan, the Data Envelopment Analysis are employee. Out of 52 microfinance providers in Pakistan, only 15 microfinance institutions is sample across the industry based on profile of gross loan portfolio of each microfinance provider. to estimate the efficiency of microfinance providers in Pakistan (i.e. constant returns to scale, variable returns to scale and scale efficiency), Malmquist productivity Index and total factor productivity of the microfinance institutions, two input variables(loan amount disbursed, total staff) and output variables (gross loan portfolio and number of active borrowers) are used. The results of the study conclude that MFIs in Pakistan are working below their optimum scales measurements and only one microfinance provider (Khushali Bank) out of 15 in our sample in 2007 and (Thardeep rural support program) in 2016 works on efficient frontier and while others are inefficient. It recommended that the institutions should increase loan amount disbursed and invest resources to the train their staff. Moreover, microfinance providers should expand by increasing number of offices to assist community.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".