Bibliographic record
Abstract
This paper analyzes the demand and supply side data to show the status of financial inclusion in Ethiopia. Using a set of survey instruments on demand side and data on supply side of financial inclusion, we first analyzed the demand side survey on account, saving, credit, payment, insurance and financial resilience as well as barriers to financial inclusion. Then, we determined and analyzed the supply side of the financial inclusion such as trends in the number of deposit accounts and loan accounts, branch per capita, branch density, ATMs per capita and ATMs density and the retail payment instruments penetration. The analysis of supply side study covered the data for the period from 2006 to 2015. We find that in Ethiopia 33.86 percent of adults has an account at formal financial institution in the year 2016. They use their account to keep money safe, send and receive payments, and to get credit services and foreign exchange services. Using the data on the supply side of financial inclusion in Ethiopia as of December 2015, we find that the branch per capita and branch density of 5.54 and 3.09 respectively. We find that barriers to financial inclusion such as lack of money, distance, fixed cost, and documentations are important obstacles in Ethiopia.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".