The Effect of Economic Growth, Imports and Exports on Food Inflation in ASEAN Countries: Case Study of Timor Leste, Laos, Cambodia, and Myanmar
Bibliographic record
Abstract
This study aims to evaluate the effect of economic growth, imports and exports on food inflation in four ASEAN countries, namely Timor Leste, Laos, Cambodia and Myanmar, over the period 2003 to 2024.This study fills a gap in the literature that rarely explores the simultaneous interaction between these variables in the context of food inflation in ASEAN countries with high inflation rates.The method used in this study is panel data analysis with Fixed Effect model.This model was chosen after a series of statistical tests, including the Chow test and Hausman test, which showed that the Fixed Effect model is more suitable for handling cross-country variation and overcoming potential heteroscedasticity and multicollinearity problems.The results show that economic growth has a significant negative effect on food inflation, while import and export variables show no statistically significant effect.These findings highlight the importance of policies that support domestic economic growth as a way to control food inflation.The main contribution of this study is the provision of new insights into how macroeconomic factors such as economic growth, imports and exports affect food inflation in ASEAN countries.The results are expected to serve as a reference for policymakers in formulating more effective economic strategies to address food inflation challenges in the region.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".