The Driving Forces of Agricultural Decline:A Panel‐Data Approach to the Italian Regional Growth
Bibliographic record
Abstract
This paper investigates the causes of the long‐run decline of agriculture during economic growth. Within a two‐sector model, agricultural decline is explained by three basic driving forces (relative price change, Rybczynski effect, and technological gap). In a dynamic (vector autoregression) specification the share of agriculture on gross domestic product the agricultural relative price and the capital intensity are simultaneously determined. The model allows for short‐run adjustments with respect to long‐run equilibrium and for cross‐sectional dependence taking into account interregional linkages. The approach is applied to the panel data set of 20 Italian regions over the period 1951–2002 of intense economic development but still persistent regional disparities. Regularities and differences of decline patterns across regions are investigated. Le présent article examine les causes du déclin à long terme de l’agriculture en période de croissance économique. Dans un modèle à deux secteurs, trois éléments fondamentaux (le changement dans les prix relatifs, l’effet de Rybczynski et l’écart technologique) expliquent le déclin de l’agriculture. Dans une spécification dynamique (VAR), la part de l’agriculture dans le PIB, le prix relatif agricole et l’intensité du capital sont simultanément déterminés. Le modèle permet des ajustements à court terme par rapport à l’équilibre à long terme et permet une dépendance transversale qui tient compte des liens interrégionaux. Ce modèle a été appliquéà un ensemble de données de panel regroupant 20 régions de l’Italie, durant la période de développement économique intense de 1951 à 2002, qui présentait des disparités régionales persistantes. Nous avons examiné les similitudes et les différences des déclins observés à l’échelle des régions.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".