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Record W2994764135 · doi:10.4309/jgi.2019.43.3

Effects of Gambling on the Welfare of Nigerian Youths: A Case Study of Lagos State

2019· article· en· W2994764135 on OpenAlexvenueno aff
Saidi Atanda Mustapha, Oluwafemi Sunday Enilolobo

Bibliographic record

VenueJournal of Gambling Issues · 2019
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsUnemploymentYouth unemploymentWelfareTax revenuePopulationDemographic economicsEconomicsWelfare economicsLabour economicsEconomic growthSociologyDemographyPublic economics

Abstract

fetched live from OpenAlex

With the increasing rate of youth unemployment in the country, Nigeria’s youths have invested their time, money, and intrinsic efforts in several gambling avenues, such as Baba Ijebu, Naira Bet, Western Lotto Bet, and others. These commitments provide them with financial resources to meet daily expenses, augment low incomes arising from unemployment, and help mitigate the rising crime rate that results from the high unemployment rate. The overall unemployment rate for the population is 70%, and youth unemployment accounts for about 58%. This study examined the participation of youths in gambling in Lagos State and the effects of youth gambling on household welfare and spending. A two-stage survey design was used: a qualitative component comprising targeted focus group discussions and probit modelling to estimate the importance of the identified challenges. The results showed that Nigeria’s unemployed youths have an intense interest in gambling to sustain their income sources and to meet their daily spending needs. These activities have reduced crime rates orchestrated by youths, with rising displacement effects on household welfare and spending. Gambling thus has adverse effects on youth welfare. The authors recommend that the Lagos State Government embark on formalizing all gambling activities to protect gambling youths, as well as on strengthening tax revenue collection through the introduction of a “win tax.” RésuméLe Nigéria connaît un taux croissant de chômage chez les jeunes, et les jeunes Nigérians investissent leur temps et leur argent et consacrent d’intenses efforts dans plusieurs domaines de jeu tels que le Baba Ijebu, le Naira Bet, le Westen Lotto Bet, etc. pour tenter de couvrir leurs dépenses quotidiennes. Cela contribue à réduire le taux de criminalité qui aurait été important en raison du taux élevé de chômage qui est de 70 % pour l’ensemble de la population, et d’environ 58 % chez les jeunes. Compte tenu de ce taux élevé chez les jeunes dans le pays, l’étude a permis d’examiner le niveau de participation des jeunes aux jeux dans l’État de Lagos. On a en outre examiné les effets du jeu des jeunes sur le bien-être et les dépenses des ménages. Les méthodes utilisées dans l’étude incluent : un plan de sondage en deux étapes, une composante qualitative qui comprend les discussions de groupe ciblées et la modélisation probit pour estimer l’importance des défis recensés. L’étude révèle que les jeunes chômeurs nigérians ont tout intérêt à jouer pour préserver leur source de revenus et couvrir leurs dépenses quotidiennes. Ces activités ont contribué à réduire le taux de criminalité observé chez les jeunes, mais on a constaté des effets de déplacement croissants sur le bien-être et les dépenses des ménages. On conclut que le jeu a des effets néfastes sur le bien-être des jeunes et on recommande donc au gouvernement de l’État de Lagos d’encadrer de manière formelle toutes les activités de jeu afin de protéger les jeunes joueurs et de renforcer le recouvrement de recettes fiscales grâce à la mise en place d’un « impôt par gagnant ».

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 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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.174
GPT teacher head0.432
Teacher spread0.257 · 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 teacher head, 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

Citations15
Published2019
Admission routes1
Has abstractyes

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