Interactions of Gross Domestic Product, External Debt and Government Expenditure: Evidence From International Development Association Countries [A Panel-VAR Approach]
Bibliographic record
Abstract
The study employed a Panel Vector Autoregressive (PVAR) model to examine the relationships among three macroeconomic variables- Gross Domestic Product, Total External Debt Stocks, and Gross National Expenditure - in International Development Association (IDA) member countries. Data from three different time frames - 1991-2019 (29 countries), 1994-2018 (35 countries), and 2008-2018 (39 countries) – was analyzed, and the lags of endogenous variables were used as instruments to address endogeneity issues in the dynamic model. The variables were transformed into growth rates to ensure stationarity and were estimated using the Generalized Method of Moments (GMM). The results were reported after removing both panel-specific and time-specific fixed effects. The study found a positive relationship between Total External Debt Stocks growth and Gross Domestic Product growth, which became more significant with the increase in the sample timeframe. The findings showed that a 100% increase in Total External Debt growth would lead to a 4-7% increase in Gross Domestic Product growth. The positive relationship was confirmed by the transmission of shocks from Total External Debt growth to Gross Domestic Product growth, but it lasted only for two periods and quickly returned to an equilibrium state. The relationship between Gross National Expenditure growth and the other variables was not conclusively established due to its lack of consistent and stable behavior with the other variables. The Stata package “pvar” was employed for data analysis and inferential conclusions.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".