Determinants of Cocoa Bean Trade in the International Market: Gravity Model Approach
Bibliographic record
Abstract
Cocoa beans are one of Indonesia's primary export commodities, ranking fourth in terms of foreign exchange earnings.Under conditions of free trade, this study aimed to analyze the determinants of the export value of Indonesian cocoa beans compared to its competitors in West African countries (Ivory Coast, Ghana, and Nigeria).The research method used is the gravity model with panel data from 2000 -2020, using STATA 14.2 and Microsoft Excel.The results indicate that the variables significantly affecting the export value of cocoa beans are economic distance, production, export volume, the population of exporting countries, harvested area, exchange rate, and membership in AFCFTA.Other variables are not significant.This study concludes that the effect of export volume and production of cocoa beans, economic distance, and exchange rate are positive, while the effect of membership in AFCFTA is negative.However, this study is unable to analyze the factors that influence the trade of Indonesian cocoa beans and its competitor countries to the import destination country (Malaysia) as well as the export of cocoa beans (raw or roasted).Future research direction includes exploring Indonesia's post-COVID-19 international trade strategy and food safety issues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".