State Borrowing and Electricity Tariff in an Emerging Economy: Post-COVID-19 Experience
Bibliographic record
Abstract
As the debt stock level of Ghana continues to rise, partly due to the negative impact of COVID-19, a number of new taxes have been introduced in the 2021 budget statement alongside an upward adjustment of electricity tariff. State borrowing may significantly influence electricity tariff, as power generation and distribution are primarily undertaken by state-owned companies whose borrowing constitutes a substantial portion of the country’s overall debt. Hence, this paper assesses the impact of state debt on electricity tariff in Ghana post COVID-19. The autoregressive distributed lag (ARDL) model and error correction model (ECM) are employed to test for the Granger causality between state debt and electricity tariff. Other variables such as inflation rates, exchange rates, and net energy imports that have the propensity to influence electricity tariff are also examined. The results reveal that state debt has both short-term and long-term impacts on electricity tariff. Additionally, inflation rates, exchange rates, and net energy imports only have long-term impacts on electricity tariff. Meanwhile, exchange rates have short-term effects on state debt. The findings imply that effective debt management policies should be implemented by the government to reduce borrowing, particularly when such borrowing is not invested into projects that can repay the debt at maturity. This study demonstrates that all the accumulated debt prior to and during the COVID-19 era is causing a significant increase in Ghana’s electricity tariff. This provides an empirical clue as to what the situation is likely to be in other developing countries.
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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 | 0.001 |
| 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 | 0.001 |
| 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".