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Record W1936101682 · doi:10.1111/jeea.12114

THE WELFARE COST OF LAWLESSNESS: EVIDENCE FROM SOMALI PIRACY

2014· article· en· W1936101682 on OpenAlexfundno aff
Timothy Besley, Thiemo Fetzer, Hannes Mueller

Bibliographic record

VenueJournal of the European Economic Association · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMaritime Security and History
Canadian institutionsnot available
FundersEconomic and Social Research CouncilInternational Growth CentreMinisterio de Ciencia e InnovaciónFetzer InstituteCanadian Institute for Advanced ResearchMinisterio de Economía y Competitividad
KeywordsSomaliWelfareRevenueContext (archaeology)LawlessnessOrder (exchange)EconomicsDeadweight lossBusinessInternational economicsLawMarket economyPolitical scienceGeographyFinance

Abstract

fetched live from OpenAlex

In spite of general agreement that establishing the rule of law is central to properly functioning economies, little is known about the cost of law and order breakdowns. This paper studies a specific context of this by estimating the effect of Somali piracy attacks on shipping costs using data on shipping contracts in the dry bulk market. To estimate the effect of piracy, we look at shipping routes whose shortest path exposes them to piracy and find that the increase in attacks in 2008 led to around an 8% to 12% increase in costs. From this we calculate the welfare loss imposed by piracy. We estimate that generating around 120 USD million of revenue for Somali pirates led to a welfare loss in excess of 630 USD million, making piracy an expensive way of making transfers.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.016
GPT teacher head0.246
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations70
Published2014
Admission routes1
Has abstractyes

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Same venueJournal of the European Economic AssociationSame topicMaritime Security and HistoryFrench-language works237,207