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Record W2292722759 · doi:10.1287/mnsc.2015.2329

On the Effectiveness of Uniform Subsidies in Increasing Market Consumption

2016· article· en· W2292722759 on OpenAlex
Retsef Levi, Georgia Perakis, Gonzalo Romero

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueManagement Science · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain and Inventory Management
Canadian institutionsUniversity of Toronto
FundersAir Force Office of Scientific ResearchDivision of Civil, Mechanical and Manufacturing InnovationNational Science Foundation
KeywordsSubsidySocial plannerConsumption (sociology)MicroeconomicsEconomicsBudget constraintPlannerWelfareConstraint (computer-aided design)Industrial organizationPublic economicsComputer scienceMarket economyMathematics

Abstract

fetched live from OpenAlex

We study the problem faced by a central planner trying to increase the consumption of a good, such as new malaria drugs in Africa. The central planner allocates subsidies to its producers, subject to a budget constraint and endogenous market response. The policy most commonly implemented in practical applications of this problem is uniform, in the sense that it allocates the same per-unit subsidy to every firm, primarily because of its simplicity and perceived fairness. Surprisingly, we identify sufficient conditions of the firms’ marginal costs such that uniform subsidies are optimal, even if the firms’ efficiency levels are arbitrarily different. Moreover, this insight is usually preserved even if the central planner is uncertain about the specific market conditions. Further in many cases, uniform subsidies simultaneously attain the best social welfare solution. Additionally, simulation results in relevant settings where uniform subsidies are not optimal suggest that they induce a nearly optimal market consumption. On the other hand, if the firms face a fixed cost of entry to the market, then the performance of uniform subsidies can be significantly worse, suggesting the need for an alternative policy in this setup. This paper was accepted by Yossi Aviv, operations management.

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.

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)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.228
Teacher spread0.208 · 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