Financial innovation and money demand in Nigeria: Exploring the impact of banking innovations, fintech developments and digital payment channels on the money demand
Bibliographic record
Abstract
The paper contributes to the frontier of knowledge by examining the role of banks and Fintech innovations across the different financial innovation platforms on four money-demand models. Quarterly data from Q1 2009 to Q4 2022 were adopted by employing the Feasible Quasi Generalized Least Squares technique, the CUSUM and CUSUMSQ techniques, and the Granger causality methods due to their suitability for running this type of regression. The results indicate that the value and volume of payments within the banking industry have a significant positive impact on the demand for reserve money. In contrast, the value of payments had adverse effects on the demand for narrow money, broad money, and total money. The significant positive impact of payment value and volume on the demand for reserve money indicates that as transactional activities increase, banks require more reserves. The negative impact of payment value on the demand for narrow, broad, and total money suggests a substitution effect where higher transaction volumes reduce the need for holding other forms of money. Furthermore, the findings demonstrated that money demand is unstable, implying that the monetary authorities should provide the transition framework for a proper inflation-targeting strategy to achieve its key objective of attaining low and stable inflation. The instability in money demand suggests that traditional monetary aggregates may not be reliable indicators for monetary policy. Therefore, the central bank should adopt a more flexible and responsive framework, such as inflation targeting, which focuses directly on achieving price stability rather than relying on intermediary targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".