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Record W3122088401 · doi:10.1093/ej/ueaa030

Outside Options, Coercion, and Wages: Removing the Sugar Coating

2020· article· en· W3122088401 on OpenAlexaff
Christian Dippel, Avner Greif, Daniel Trefler

Bibliographic record

VenueThe Economic Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsCanadian Institute for Advanced Research
Fundersnot available
KeywordsCoercion (linguistics)AgricultureEvictionInformal sectorLabour economicsEconomicsGovernment (linguistics)Value (mathematics)BusinessMarket economyPolitical scienceGeographyLaw

Abstract

fetched live from OpenAlex

Abstract In economies with a large informal sector firms can increase profits by reducing workers’ outside options in that informal sector. We formalise this idea in a simple model of an agricultural economy with plantation owners who lobby the government to enact coercive policies—e.g., the eviction and incarceration of squatting smallhold farmers—that reduce the value to working outside the formal sector. Using unique data for 14 British West Indies ‘sugar islands’ from 1838 (the year of slave emancipation) until 1913, we examine the impact of plantation owners’ power on wages and coercion-related incarceration. To gain identification, we utilise exogenous variation in the strength of the plantation system in the different islands over time. Where planter power declined we see that incarceration rates dropped, and agricultural wages rose, accompanied by a decline in formal agricultural employment.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.062
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.050
GPT teacher head0.282
Teacher spread0.232 · 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

Citations29
Published2020
Admission routes1
Has abstractyes

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