Financial Literacy and Entrepreneurship as Solutions to Poverty in Goma City, the Democratic Republic of the Congo
Bibliographic record
Abstract
Goma, in the Democratic Republic of the Congo (DRC) is an entrepreneurial city. Everyone whether they are financially educated or not, want to do entrepreneurship by engaging in various activities. But it is not every entrepreneur who succeed to grow their small enterprises to successful business. The objective of this paper is to find out whether financial literacy and entrepreneurship are solutions to poverty in Goma City DRC by interviewing a sample of eighty-six entrepreneurs who are registered and operate in Goma City. The purposive and stratified samples were randomly picked from a population of one hundred and ten entrepreneurs where questions in financial literacy, entrepreneurship and poverty alleviation were asked. The methodology used was quantitative and was aimed at depicting the manner in which financial literacy and entrepreneurship affect poverty alleviation. Aspects of qualitative research were also used through observation. The study made use of financial literacy and entrepreneurship scores against the level of poverty solutions per questionnaire. The research used both primary and secondary data but mainly primary data using a self-structured questionnaire. Data analysis was performed with the aid of SPSS version 16.0, Google Form, Excel and STATA version 15.0 using both descriptive and inferential statistics, econometrics and structural equations modeling. The data collected was then analyzed to establish relationship between financial literacy, entrepreneurship and poverty solutions. From the research findings, all entrepreneurs interviewed were found to have some level of financial literacy and acceptable socioeconomic status and high living standard. In contrast, less successful entrepreneurs exhibited stagnant growth, low level of financial literacy, low socioeconomic status and low living standard and majority of them were found to be in Finance and insurance sector. This study concludes that entrepreneurship is a direct solution to poverty while financial literacy is an indirect solution to poverty in Goma city through entrepreneurship. It further recommends that Government through the Ministry of Entrepreneurship of DR Congo must ensure that entrepreneurs are highly financial literate in order to increase their business rationality and profitability. Furthermore, it suggests that the Government must create the best business climate in order to encourage the entrepreneurship in Goma City.
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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) | 0.001 | 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".