Taxation Reforms: a CGE-Microsimulation Analysis for Pakistan
Bibliographic record
Abstract
This paper provides an ex ante assessment of taxation reforms being considered in Pakistan, in order to widen the tax base and rationalise the rate structure of different taxes. Amongst the main proposals, those focusing on sales tax and agricultural direct taxes seem relatively more attractive. The former has the highest share in indirect taxes and is also easier to collect, while the latter is intended to bring the presently exempted agricultural incomes into the tax net. As a first step, we study the general equilibrium effects of existing taxes by removing them from the system one at a time. In the second step we study the micro-macro impacts of four policy experiments: a) increasing sales tax rate by 33 percent; b) applying a 10 percent sales tax on presently zero-rated goods; c) increasing sales tax rate by 33 percent and bringing the services sectors in the sales tax net; and d) increasing sales tax rate by 33 percent, bringing the services sectors in the sales tax net, and imposing a 5 percent flat tax on agricultural incomes. In the third step we calculate the lost revenue due to evasion and avoidance. Results from experiments indicate the tough choices for policy makers in trying to improve the currently low tax to GDP ratio in Pakistan. Almost all simulations result in a decrease in investment levels, reduced consumption, and an increase in poverty. We thus recommend a gradual approach to tax reform that can make the adjustment process less painful.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".