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Record W2605172918 · doi:10.5539/ijef.v9n4p262

Savings Habit Among Individuals in the Informal Sector: A Case Study of Gbegbeyishie Fishing Community in Ghana

2017· article· en· W2605172918 on OpenAlexvenueno aff
Bismark Addai, Adjei Gyamfi Gyimah, Wendy Kumah Boadi Owusu

Bibliographic record

VenueInternational Journal of Economics and Finance · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsInformal sectorProbit modelHabitFishingDescriptive statisticsProbitOrdered probitDemographic economicsSample (material)Propensity score matchingEconomicsEstimationSocioeconomicsBusinessEconomic growthEconometricsPsychologyStatisticsPolitical science

Abstract

fetched live from OpenAlex

Savings among individuals in the informal sector is imperatively expedient if they are to have any decent and comfortable living conditions at retirement as savings in the informal sector become the obvious substitute for formal pensions. However, much is not known regarding the savings habits of informal sector, particularly, the fishing communities in Ghana. Apparently, this study investigates into the determinants of savings habit of the informal sector in Ghana, using the case of the Gbegbeyishie Fishing community. The data for the study was obtained through administering questionnaires and interviewing targeted respondents. A 120 sample size was randomly drawn from Gbegbeyishie fishing community in Ghana. This study employs the probit model in estimating the determinants of savings in the informal sector. SPSS and STATA statistical packages were employed in descriptive analysis and estimation of the probit model respectively.It is glaring in this study that age, gender and income are statistically significant conditions for savings in the informal sector. It is also evincing in this study that Age has a significant negative effect on savings and aging decreases the propensity to save by 0.1577656. On the other hand, income has statistically significant positive effect on savings and that a one unit change in the income variable increases the propensity to save by 0.1292502. Also, the probability for a male, all other factors held constant, to save is higher than for a female to save and being a man increases the propensity to save by 0.2024894. The study also revealed that the main hindrance to savings in the Gbegbeyishie Fishing Community is Low income.As a result, the authors recommend that men and married people should be targeted whiles paying little attention to the aged in stimulating savings among fishing communities in Ghana. Educational programs could also be organized for the workers in the informal sector as most of the workers have no education which could hinder their income earning capacity and for that matter savings. Further research could also be engineered to consider macro-economic conditions for savings habit in Ghana.

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)0.002
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 score0.030
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.030
GPT teacher head0.255
Teacher spread0.225 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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