An Empirical Analysis of the Impact of Agency Banking on Financial Inclusion in Benue State, Nigeria: Implications for Economic Activities
Bibliographic record
Abstract
Twelve (12) out of the Twenty-three (23) local government areas (LGAs) in Benue State do not have the presence of banks over a long period of time. This situation has deprived the inhabitants of these LGAs of access to formal financial services until the advent of agency banking. This study therefore, investigates the impact of agency banking on financial inclusion and economic activities in Benue State focusing on the agency banking activities of First Bank Ltd. The study is anchored on the agency theory and it used a survey design. The study has utilized both primary and secondary data that were analyzed using descriptive statistical tools and structural equation models. Findings of the study have revealed that agency banking activities of First Bank Ltd have immensely enhanced financial inclusion and economic activities in Benue State. However, challenges such as shortages of cash, security problems, network failures, and lack of financial literacy are militating against the smooth operations of the agency banking in the State. On the basis of these findings, the study has recommended among others that, other banks operating in the State should be encouraged to venture into agency banking in the state so as to have a wider coverage of agency banking in the State. Also, government should provide security and partner with the private sector to provide national carrier communication network system to overcome the network failure challenge. Finally, banks should intensify efforts to educate the masses about the validity and potency of agency banking.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".