Impact of Foreign Direct Investment on Agriculture, Forestry and Fisheries: Evidence from Eastern African Countries
Bibliographic record
Abstract
Agriculture, forestry, and fishing play a crucial role in strengthening the country’s economic development, particularly in supporting industries, and have evolved in Africa in recent years. This study aims to analyze the impact of foreign direct investment on agriculture, forestry, and fisheries in 5 Eastern African countries (Djibouti, Ethiopia, Kenya, Rwanda, and Tanzania). Using the panel dataset on the 5 Eastern African countries selected for the period 2013 to 2022, we employ the panel data regression method, more specifically, the fixed-effect panel model. For the empirical analysis, correlation analysis and Granger Causality are also employed to analyze the relationship between the variables present in this study. Using the Granger Causality test result, the study highlights that there is a one-way relationship between foreign direct investment and agriculture, forestry, and fisheries. The results of the study reveal that foreign direct investment has a positive impact on agriculture, forestry, and fisheries. Furthermore, findings suggest that capital formation, export trade, and government spending have a positive impact on agriculture, forestry, and fisheries. Therefore, it is recommended that East African governments take the necessary steps to strengthen and attract foreign direct investment, which can improve the agriculture sector and achieve sustainable economic development.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".