Valued Added Tax (VAT) Impact on Economic and Societal Well-Beings (Pre-and Post COVID19): A Perception Study From Saudi Arabia
Bibliographic record
Abstract
The Kingdom of Saudi Arabia (KSA) has realized that merely relying on oil reserves, as the main income source will stop the country from achieving sustainable economic growth due to the instability of world oil market prices and its uncertainties. Hence, the first Value Added Tax (VAT) has been introduced in KSA in 2018 following to the urge by International Monetary Fund (IMF). However, VAT implementation in the Kingdom is not free from criticisms since its citizens, particularly younger generation, have been used to tax-free livings. In addition, the global health pandemic of COVID19 has hit major economies worldwide. In correspondence, KSA has decided to increase its VAT rate to 15% from 5% by the 1st of July 2020, which may suggest a different pattern of consumption behaviour since the rate has been drastically increased. Therefore, this study aims to examine the perception of Saudi younger generation on the acceptance of VAT based on five categorical factors underlying Saudi younger generation’s level of acceptance or rejection of VAT, namely; tax system effectiveness (TSE), income generation sources (IGS), income inequality (II), system knowledge (SK) and global practice (GP). The study also is set to examine the impact of VAT on Saudi younger generation’s consumption behaviour. Using a sample of 460 respondents, the findings reveal that most of the respondents (89.6%) are aware of VAT implementation. However, the negative perceptions found from the analysis shows that VAT is not an efficient tax system to be implemented (average TSE = 45.6% less favourable) as the VAT system does not provide income inequality among citizens (average II= 45.3 percent less favourable). Moreover, the results also show that VAT implementation does not affect the consumption behaviour, suggesting that Saudi younger generation still spends regardless of VAT. Limited studies among the GCC academics on VAT has motivated this research to be among the first attempt to document such impact and to consider it in lights of new legislative changes due to COVID-19. Hence, this study is both timely and relevant to provide new understandings to responsible authorities regarding its VAT system, its acceptance and impacts on consumption behaviour in lights of COVID-19.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".