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Record W4309351296 · doi:10.5539/ass.v18n11p17

Do Wage Subsidies to Nationals Enhance Their Employability? New Evidence from Kuwait

2022· article· en· W4309351296 on OpenAlexvenueno aff
Maurice Girgis, Raouf SHanna, Shaikha Al-Fulaij

Bibliographic record

VenueAsian Social Science · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
FundersKuwait Institute for Scientific Research
KeywordsSubsidyLabour economicsEconomicsEmployabilityUnemploymentWagePrivate sectorEconomic growthMarket economy

Abstract

fetched live from OpenAlex

Due to the persistent unemployment of nationals in the midst of millions of gainfully employed foreign workers, Kuwait, the focus of this study, introduced a new generation of active labor market policies (ALMPs) in 2000, capped by an expansive wage subsidy to nationals who join the private sector. This study employs the Fully Modified Least Square (FM-OLS) model to evaluate the effectiveness of these policies by means of estimating employment elasticities of Kuwaitis in the private sector in response to wage subsidies. The results show that the subsidies have been ineffective, contrary to their well-documented broad positive impact in industrial economies. This outcome has been attributed generally to wage differentials between Kuwaitis and non-Kuwaitis. This study presents a holistic diagnosis of the problem by challenging the received ‘wage differentials’ as the sole cause. It identifies a host of heretofore-unidentified structural disequilibria in the labor market that are responsible for the subsidy’s ineffectiveness such as the local economy’s inherent bias towards low-tech methods of production that rely heavily on low-skilled labor, Kuwaitis’ penchant to pursue higher levels of education, low national participation rates in the labor force, the absence of critical vocational training programs, and the government’s liberal immigration policies. Based on the revised prognosis, a new integrated remedial plan of actionable steps is suggested to address the unemployment problem.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.130
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.092
GPT teacher head0.276
Teacher spread0.184 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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