Asymmetric Effects of Fiscal Deficit Financing and Inflation Dynamics in Ghana
Bibliographic record
Abstract
Fiscal Deficit Financing (FDF) has been unsustainably high in Ghana and this has led to unstable and high inflation episodes since 1980. The FDF averaged 4.6% from 2005-2011 and 6.9% to 2012-2018, while inflation averaged 11.0% and 13.1% relative to medium-term to long-term inflation target of 8.0% in the same periods, respectively. Previous studies on deficit financing-inflation nexus in Ghana have primarily focused on linear and symmetric relationship, thereby ignoring the asymmetric policy effects of FDF on inflation dynamics. Disregarding the asymmetry of FDF could impact negatively on efforts of Bank of Ghana in forecasting and controlling inflation effectively. To address this problem, this study was therefore designed to investigate the asymmetric policy effects of FDF on inflation dynamics in Ghana over the period 1980-2018. The fiscal theory of the price level provided the theoretical framework. The Non-linear Autoregressive Distributed Lag (NARDL) econometric methodology was deployed to examine the asymmetric effects of FDF on inflation dynamics. The paper found that FDF had asymmetric effects on inflation dynamics in Ghana as the positive outcome of FDF had a significant positive asymmetric effect of 29.0% on inflation while its negative outcome had a relatively less asymmetric effect of 22% on inflation dynamics, suggesting that consolidating fiscal policy was disinflationary. The paper finally recommends that fiscal authorities should adopt consolidating and prudent fiscal policies that could lead to fiscal solvency and sustainability, which could potentially moderate the effect of FDF on inflation dynamics.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".