THE IMPACT OF COVID-19 ON INTERNALLY GENERATED REVENUE OF SOUTH-WEST NIGERIA
Bibliographic record
Abstract
Purpose: the paper investigated the impact of COVID-19 on the Internally Generated Revenue (IGR) of southwest Nigeria comprising Ekiti state, Lagos state, Ogun state, Ondo State, and Osun state, Oyo state. Methodology: the authors sourced data from secondary sources; the Internally Generated Revenue was obtained from the annual publication of the National Bureau of Statistics covering 2019 and 2020 and the COVID-19 confirmed cases were obtained from National Disease Control Centre. Findings: The result showed that paired correlation of IGR 2019 and 2020 showed a strong positive correlation and the same was also true of COVID-19 cases and IGR 2020 (p = 0.001). The result of the t-test showed no significant difference (p > 0.05) between IGR 2019 and IGR 2020 quarter on quarter. Unique Contribution to Theory, Practice and Policy: The result supported two theories; The ‘Pecking Order Theory and Ability-To-Pay, Internally Generated Revenue focuses on funds derived within the state, just like internal financing to a firm. The internally generated revenue did not decline during the pandemic because taxes were paid since the majority were paid salaries during the pandemic and transactions were conducted online via platforms, more importantly, the Central Bank of Nigeria did not shut down so economic activities were not paralyzed but migrated to online. The study proved that in times of crisis, IGR may not be adversely affected if all the channels of generating income are available to taxpayers. This aids budget and planning during a crisis period, furthermore, the government will be able to plan and channel funds to areas of need accordingly. The result further supported the new model of working; people can work remotely and pay their taxes despite the lockdown.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".