Determinants of Access to Financial Services among Adults in Tanzania: The Evidence from FinScope Tanzania Survey (2017)
Bibliographic record
Abstract
There is low financial inclusion across developing countries, especially those in Sub-Saharan Africa (SSA) including Tanzania. Almost three quarter of the SSA citizens don’t hold any form of account with a formal financial institution. East African countries have poor access to financial services especially the highly populated rural areas. The study examined the determinants of access to financial inclusion among adults in Tanzania. The study used a longitudinal research design to access data collected from 9459 respondents who were selected from the FinScope Tanzania 2017 which were weighted by NBS. Data were analyzed using both descriptive and Binary Logistic Model as inferential statistics with aid of STATA version 16. The study was able to find out that individual savings as a key determinant to financial inclusion was relatively low this being due to income gap, education and access to technology. Most of the respondents were willing to save but many-faced constraints to save. finally, the determinants of access to financial services in Tanzania among adults was identified to be age, education level and income. The study concluded that financial inclusion (access to financial services) among adults in Tanzania was mainly determined by age, education and income. Therefore, the effort should be made by the government to stimulate technological adoption so as to enhance variety of service delivery mechanism and provide financial knowledge to the society to inform people to make proper decisions regarding their financial well-being.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".