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Record W2008230484 · doi:10.1002/pam.1005

Testing a Financial Incentive to Promote Re‐employment among Displaced Workers: The Canadian Earnings Supplement Project (ESP)

2001· article· en· W2008230484 on OpenAlexaffabout
Howard S. Bloom, Saul Schwartz, Susanna Lui‐Gurr, Suk‐Won Lee, Wendy Bancroft

Bibliographic record

VenueJournal of Policy Analysis and Management · 2001
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsCarleton University
Fundersnot available
KeywordsReceiptEarningsIncentivePaymentUnemploymentDisplaced workersBusinessLabour economicsDemographic economicsEconomicsFinanceAccountingEconomic growth

Abstract

fetched live from OpenAlex

Abstract This article presents findings from a randomized experiment conducted in four Canadian provinces to measure the effects of a generous financial incentive that was designed to promote rapid re‐employment among workers who were displaced from their jobs by changing economic conditions. The incentive tested was an earnings supplement which, for as long as 2 years and as much as $250 weekly, would replace 75 percent of the earnings loss incurred by displaced workers who took a new lower‐paying full‐time job within six months of receiving a supplement offer. Findings from the experiment indicate that although persons offered the supplement understood its terms and conditions, only 2 out of 10 actually received supplement payments. Furthermore, the supplement offer had little effect on job‐search behavior, employment prospects, or receipt of unemployment insurance. Nevertheless, persons who received supplement payments benefited from them substantially. On average, they received payments for 64 weeks, totaling $8,705. © 2001 by the Association for Public Policy Analysis and 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.

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)0.005
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.825

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.028
GPT teacher head0.271
Teacher spread0.243 · 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

Citations20
Published2001
Admission routes2
Has abstractyes

Explore more

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