The impact of direct and indirect taxation on poverty: The case study of Pakistan
Bibliographic record
Abstract
This research investigates the impact of direct and indirect taxes on poverty in Pakistan, recognizing poverty as a global issue prevalent across developed, developing, and underdeveloped nations. Utilizing time series data spanning from the first quarter of 2000 to the fourth quarter of 2021, we employed an Autoregressive Distributed Lag (ARDL) model to assess short-term and long-term relationships between poverty and various tax variables including sales taxes, custom taxes, income taxes, and federal excise duties. Additionally, inflation and unemployment were incorporated as control variables in the analysis. By employing Akaike Information Criteria (AIC) to determine variable lags and conduct long-run bound tests to evaluate the significance of relationships, our findings highlight a significant association between poverty and sales taxes, federal excise duties, and income taxes. Custom taxes were found to have no significant impact on poverty in thelong run but increased poverty in the short term, while sales taxes and income taxes exhibited short-term effects on poverty. Overall, indirect taxes were identified as contributing to increased poverty levels in the short term. These results underscore the importance of understanding the nuanced impacts of taxation policies on poverty dynamics, emphasizing the role of targeted interventions in poverty alleviation efforts in Pakistan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".