The Impact of Covid-19 and Lockdown on South African Revenue
Bibliographic record
Abstract
The state revenue plays a critical role in the running of its departments and plays a significant role in the economy. The study investigates the impact of COVID-19 and the lockdown on the South African revenue collections. The study focuses on the major taxes Personal Income Tax (PIT), Corporate Income Tax (CIT) and Value Added Tax (VAT). Over the fiscal year 2008/09 to 2019/20 the three taxes contribute around 80% of the Total Tax (TTAX). The sample data was from quarter 1, 2014 to quarter 2, 2020 (50 observations) obtained with quarter 2 of 2020 carrying the impact of the pandemic. The SARIMA and Holt-Winters models were used to forecast the continuation of the historical patterns in two scenarios, (1.) Without the impact of the COVID-19 pandemic (No shock), and (2.) with the impact of COVID-19 and lockdown (with a shock) on the revenue collections. The R-statistical software was used to obtain the regression parameters and for forecasting purposes. On “average”, the impact of the pandemic is expected to reduce total revenue by around R310.6bn to R1, 127 trillion (on the interval R1, 093 – R1, 162 trillion) from the original estimates of R1, 438 trillion for the fiscal year 2020/21. The average forecast for PIT, CIT and VAT due to the impact of the pandemic is R532.9billion, R172.6billion and R320.9billion respectively. The study further encourages model revision as more data impacted by the pandemic become available.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".