Impact of Taxation on Price Formation in Agricultural Markets: Example from Antalya Greenhouse Production
Bibliographic record
Abstract
Taxation of economic activities is inevitable for formation and maintenance of national budgets. However, the level and payment structure of the taxes and reactions of taxpayers should be considered carefully in the scope of proper management of the taxation system. Agriculture, being the first taxed sector, provides limited insight for assessment of taxation systems. With the field survey conducted with 281 glass and plastic house producers from Antalya, Turkey, it was aimed to understand the impacts of taxes payed on the profitability of farm enterprises as well as main socio-demographic factors. It was found that the enterprises, 76% of which are taxed on real income, achieved to produce 7.190,73 TL (2.463,25 USD) profit per 1.000 m2. Following calculation of the profit level, it was intended to analyse the factors effecting profit inefficiencies of the farm enterprises.Accordingly, stochastic profit frontier was estimated with the data retrieved and it was concluded that both the direct income tax and indirect value-added tax could be used as policy tools to increase profitability and attain sustainability of greenhouse production sector, which is a dominant sector in the Mediterranean region of Turkey. That’s why both tax indicators seemed to be in negative relation with the profit level and there is a possibility to reduce the inefficiency specifically with a VAT revision for the inputs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".