Growth in Total Factor Productivity in the Egyptian Agriculture Sector: Growth Accounting and Econometric Assessments of Sources of Growth
Bibliographic record
Abstract
This research aims to assess the Total Factor Productivity (TFP) of the whole agricultural sector in Egypt for the period 1961-2012 using Törnqvist index calculations. Particularly, it aims to investigate: (1) the most important factors explaining the TFP growth in the Egyptian agriculture (2) estimating changes in technical efficiency and technical change and determining the magnitude of their contribution to the overall TFP growth, and lastly, (4) based on these findings, providing policy implication and recommendations that allows enhancing and sustaining future growth of agricultural production in Egypt. The currently analysis provided relevant results which might help us understanding the structural trend of the Egyptian agricultural sector, and understanding the most significant variables affecting this trend. Such results will have important policy implications for promoting further growth in the Egyptian agricultural sector. The empirical findings showed that rural development variables were found to significantly and negatively affect agricultural productivity. This demonstrates that agricultural activity is still a marginalized activity which is linked to low levels of income and is a source of employment for low productive labor. Moreover, a negative significant effect of the infrastructure variable on the productivity gains of the agricultural sector in Egypt was found which might indicates a form of low integration of farmers within large neighboring markets. These findings highlighted the decisions makers to take a deeper look at their rural infrastructure strategy, knowing that it may affect the productivity of the agricultural sector as whole.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".