Savings Habit Among Individuals in the Informal Sector: A Case Study of Gbegbeyishie Fishing Community in Ghana
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".