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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".