Economic Analysis of the Investments in Public Infrastructure Impacts on Agricultural Production in Benin
Bibliographic record
Abstract
Agricultural activity is to produce food and raw material and then, it plays an important role in economic and social development of the country by contributing in the Gross production growth and by providing employment. In spite of its weight in Benin economy, the sector is characterized by the small level of the exploitation of its numerous potentials of agro ecological areas, the predominance of small exploitation sizes, its vulnerability to climatic hazard, its low productivity, mechanization and intensification levels, ect. This sub exploitation is the results of illiterate of the farmer, the uses of archaic methods and technical production and the shortcomings of direct investments in the sector such as water storage, rural roads, hydro agricultural adjustments, etc. Now, theoretical arguments and historic data reveal a narrow link between the investments in infrastructure and the economic productivity growing, and thus the different components of the economy. This paper examines the links between public infrastructures and the Benin agricultural production according to the renewal interest to improve transport, health and education infrastructures. Using Cobb-Douglas model and Benin data from 1980 to 2009, we show that the investments in infrastructures, mainly in education and transport, constitute a good politics to improve agriculture for a long time.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".