Macroeconomic Impact of Value Added Tax in Nepal: A 2SLS and 3SLS Approach
Bibliographic record
Abstract
The study’s aim to evaluate the impact of VAT on macroeconomic variables (TCON, GDP, Import (M) and GOVEXP) and the reverse effects of Macro variables on VAT. This study included macroeconomic development variables such as GDP, GDP (-1), VAT, remittance (REM), total consumption (TCON), export (X), import (M), gross fixed capital formation (GFCF), bank rate (BRATE), trade openness (TOPEN), government expenditure (GOVEXP) and one period lagged government expenditure (GOVEXP (-1)). However, this study has been enhanced over previous analyses by incorporating 45 years of nominal data from 1974/1975 to 2018/19. It examines the connection between VAT and significant macroeconomic variables such as GDP, GDP (-1), REM TCON, X, M, GFCF, BRATE, TOPEN, GOVEXP and GOVEXP (-1). To address the challenges of simultaneous equation bias and inconsistent findings, this study utilized the two-stage least squares (2SLS) and three-stage least squares (3SLS) methodologies to assess the impact of VAT on macroeconomic variables. The findings of the study are that value-added tax (VAT) negatively impacts TCON. VAT positively and significantly impacts on GDP, import(M), and GOVEXP. The study revealed that while TOPEN negatively affected Nepal’s GDP, GOVEXP, GFCF, and X had a significant and positive influence on GDP.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".