Macroeconomic Impact of Value Added Tax in Nepal: A 2SLS and 3SLS Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it