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Record W1572332380

Efficiency losses in milk marketing boards - the importance of exports

2005· preprint· en· W1572332380 on OpenAlexaboutno aff
Rolf Jens Brunstad, Ivar Gaasland, Erling Vårdal

Bibliographic record

VenueDuo Research Archive (University of Oslo) · 2005
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsProfitability indexDeregulationPrice discriminationBusinessEconomic surplusValue (mathematics)Competition (biology)EconomicsWelfareMarket economyMarketing
DOInot available

Abstract

fetched live from OpenAlex

A milk marketing board (MMB) is a legislatively specified compulsory marketing institution, and a common way to regulate markets for dairy products. MMBs are based on price discrimination. As price discrimination leads to unequal profitability between products, receipts from sales are pooled and farmers receive a single price adjusted for composition and quality. It is well documented that price discrimination through MMBs incurs an efficiency loss to the society. Earlier contributions, Ippolito and Masson (1978) and Serck-Hansen (1979), point out that a particularly high loss is incurred if export of dairy products is included in the MMB. It proves difficult to find examples where this is the case. MMB countries are either large with a low export share in dairy products (USA and Japan), have economies of scale (Australia) or exports are excluded from the MMB arrangement (Canada). However, we find Norway to be a good example. Using a numerical model of the Norwegian agricultural sector we show that substantial efficiency gain may be achieved by deregulating the dairy sector, mainly due to the elimination of exports. It is estimated that a transition to cost based pricing may increase the economic surplus by NOK 1.5 billion, which is 26% of the production value. This computed gain from deregulation is far larger than for the other MMB-countries.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.038
GPT teacher head0.262
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2005
Admission routes1
Has abstractyes

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