Does Currency in Circulation Promote Economic Performance in Developing Countries? Evidence from Nigeria
Bibliographic record
Abstract
Empirical studies on currency in circulation have been the object of great attention by economists in thedeveloping countries due to its vital role in achieving effective electronic payment system by the monetaryauthorities. In this disquisition, analysis were carried out to examine whether currency in circulation (CIC)promotes economic performance, using Vector Autoregression Model (VARM) and VAR Granger CausalityTest, annual data of all variables for the period 1960–2011 were employed. According to the results, thecoefficient of currency in circulation when lagged by one period is positive but statistically insignificant at 5%contrary to expectations. The statistically insignificant relationship that exists between the monetary instrumentssuch as; exchange rate, inflation rate, normal interest rate, high power money, currency in circulation, demanddeposit and normal GDP sheds more light on how ineffective monetary policies adopted by the Central Bank ofNigeria (CBN) for promoting economic growth. This findings brings to the fore that the cashless economy beenproposed by the CBN will have a significant impact on the performance of Nigeria economy. The governmentshould provide adequate infrastructure and all enabling legal framework that will help the informal sector of theeconomy to embrace the cashless payment system, so as not to erode the diminutive contribution of the informalsector to GDP in Nigeria. The study also revealed that demand deposit granger causes economic growth so CBNshould increase the deposit rate which will serve as incentive and enforce the existing financial regulations thatwill protect depositors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".