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Record W3153765533 · doi:10.56279/ter.v4i1-2.8

Informal Construction Employment, Earnings and Activities: A Boon or Bane for Tanzania?

2014· article· en· W3153765533 on OpenAlexfundno aff
Beatrice Kalinda Mkenda, Jehovaness Aikaeli

Bibliographic record

VenueTanzanian Economic Review · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsInformal sectorEarningsTanzaniaPovertyBusinessWork (physics)Labour economicsOrder (exchange)Economic growthDemographic economicsEconomicsFinanceSocioeconomics

Abstract

fetched live from OpenAlex

This paper assesses whether the growth of informal construction employment and activities in Tanzania are a boon or bane for informal workers. It examines the importance of employment and income provision, employment conditions, and linkages between formal and informal firms. It also examines the determinants of earnings of workers and the challenges faced by the informal construction sector. The study finds that informal construction activities are important in providing employment and income to people, although a significant number of employees work without contracts and pensions. The level of informal sector earnings is also lower than that of the formal sector. The statistically significant results from regression analysis of the determinants of earnings, which are positively related to earnings are: age, education level, and number of years of experience. The policy implications of this study include: the need for informal construction employees to be affiliated to pensions and health insurance benefits; requirement of a mechanism to enable them to formalize easily in order for them to access credit and to expand their operations; improvement in their skills to enhance their income levels so as to reduce poverty; and to empower them to share in the growth of construction activities.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.030
GPT teacher head0.294
Teacher spread0.264 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2014
Admission routes1
Has abstractyes

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