Financial Inclusion in West African Economic and Monetary Union’s Economies: Performance Analysis Using Data Envelopment Analysis
Bibliographic record
Abstract
A data envelopment analysis (DEA) has yet to be chosen to assess countries’ financial inclusion levels. We introduce an application of the DEA methodology to compute aggregate performance measures regarding the financial inclusion of economies. We specifically explore composite scores based on relative efficiency, super-efficiency, and cross-efficiency approaches. We implement the proposed procedure to study the financial inclusion in nations from the West African Economic and Monetary Union (WAEMU). We use the Union’s Central Bank’s financial inclusion data from 2010 to 2017. We obtain robust financial inclusion level measures, showing that overall, in the Union, there have been steady improvements during the study period, but with heterogenous behavior at the level of each economy. A benchmarking analysis allowed us to determine the countries with the best practices. For the remaining nations, we find their reference countries. Finally, we identified which financial service sectors drive the financial inclusion in each country from the optimal weights of the DEA model.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".