Increasing Share of Agriculture in Employment in the Time of Crisis: Puzzle or Not? (Kriz Zamanlarinda Artan Tarim Istihdami: Bilmece mi, Degil mi? )
Bibliographic record
Abstract
In the first quarter of 2008, along with the beginning of the crisis, the employment share of agriculture in Turkey deviated from its long-run trend and started to rise. Both the timing and the direction of the change caused a public debate for an explanation of this phenomenon. Less to the attention of the debate, labor productivity in agriculture has been declining since that quarter. How much of the increase in agricultural employment can be explained by the secular changes in its productivity? We use a multi-sector general equilibrium model, in which employment share in agriculture is determined solely by the subsistence constraint and labor productivity in agriculture, where sectoral productivity growth rates are treated as exogenous to answer this question. The model accounts for more than 90 percent of the decline in the agricultural employment share between 2000 : Q2 and 2010 : Q3. The model is also able to generate the increase in agricultural employment since 2008 : Q1, although it slightly overpredicts the agricultural employment share. The model also predicts the sectoral allocations of labor in non-agricultural activities during the sample period. A detailed analysis of the driving forces of the agricultural productivity growth is needed as it is at the heart of the secular changes in employment shares in Turkey.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".