Does the use of information and communication technologies improve cereal production in Sub‐Saharan Africa? A method of moments quantile regression approach
Bibliographic record
Abstract
Abstract Agriculture is a cornerstone of the Sub‐Saharan African (SSA) economy, and leveraging ICT to enhance productivity is vital for improving food security. While prior studies focus on ICT's micro‐level effects in agriculture, its macro‐level impact on SSA's cereal production remains underexplored. This study employs the method of moment quantile regression (MMQR) and a balanced panel dataset to analyze the effects of ICT adoption on cereal production across SSA from 2001 to 2022. The findings reveal that mobile phone usage significantly boosts cereal production, particularly benefiting lower‐productivity farmers. Internet access enhances yields, with its impact strengthening at higher productivity levels. Expanded network coverage also positively influences production, while fixed broadband subscriptions show a negative correlation, likely due to rural infrastructure limitations. Furthermore, the study identifies education and agricultural credit as key channels through which ICT improves cereal production. Finally, we find bidirectional causality between cereal production and mobile phone usage, internet access, and network coverage, while fixed broadband subscriptions exhibit a unidirectional causal effect. These insights suggest that policies promoting network expansion, mobile connectivity, and internet access, especially in rural areas, could significantly enhance cereal production in SSA.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".