An Empirical Investigation into Urban Informal Tire Repair Service in Ilorin, Nigeria
Bibliographic record
Abstract
The employment generation capacity of the formal sector comprising the public and organized private sectors in Nigeria is shrinking. The informal sector therefore offers a ray of hope for employment and earnings for urban unskilled and semi skilled labour. The promotion of informal sector activities by the government requires an insight into their mode of operation for better policy targeting. This paper therefore examines the operational characteristics, financing, training, employment and earnings, and challenges of tire repair business in Ilorin, Nigeria. The data were collected through a structured questionnaire and analyzed using descriptive statistics and phi coefficient. The study finds that tire repair provides employment and income for low skilled labour, most of whom are underemployed, earning about US$7.36 per day. The study finds that age, apprentice access and tire repair service index have significant association with earnings. About 92% of the operators sourced their financing for start-up capital from the informal financial sector. The informal training required is through the apprenticeship scheme, which is largely deficient in safety issues relating to tire rating, maximum load, expiry date, resistance, etc. The safety gap in the apprenticeship training scheme should be bridged through training and seminar. The informal sector support agencies should be overhauled to provide financing, technical and extension services to the informal sector operators. JEL Classification: G29; O17; R41 Key words: Informal sector; Tire repair; Operation; Employment; Road safety Resume La capacite de generation d'emplois du secteur formel comprenant les secteurs public et prive organise au Nigeria est en diminution. Le secteur informel offre donc une lueur d'espoir pour l'emploi et des revenus en milieu urbain non qualifies et semi-d'œuvre qualifiee. La promotion des activites du secteur informel par le gouvernement exige un apercu de leur mode de fonctionnement pour une meilleure politique de ciblage. Ce document examine donc les caracteristiques operationnelles, financement, formation, emploi et revenus, et les defis de l'entreprise de reparation de pneus a Ilorin, Nigeria. Les donnees ont ete recueillies par le biais d'un questionnaire structure et analysees en utilisant des statistiques descriptives et le coefficient phi. L'etude constate que la reparation de pneus fournit de l'emploi et de revenus pour le travail peu qualifie, dont la plupart sont sous-employes, gagne environ $ 7,36 americains par jour. L'etude constate que l'âge, l'apprenti et l'indice d'acces de service reparation de pneus ont une association significative avec les gains. Environ 92% des operateurs provenant de leur financement pour les start-up capital du secteur financier informel. La formation informelle est requise par le regime d'apprentissage, qui est largement deficiente dans les questions de securite relatives a la notation des pneus, la charge maximale, la date d'expiration, resistance, etc L'ecart de securite dans le schema de formation des apprentis doit etre comble par la formation et de seminaire. Les agences d'appui au secteur informel devrait etre remanie pour offrir des services de financement, techniques et de vulgarisation pour les operateurs du secteur informel. Classification JEL: G29; O17; R41 Mots cles: Secteur informel; Reparation de pneus; Fonctionnement; Emploi, Securite routiere
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".