Analysis of Factors that Influence Financial Literacy of Millennials in Canada
Bibliographic record
Abstract
Financial literacy has been recognized as a key skill that equips us with knowledge to manage our financial resources effectively, especially in an increasingly complex financial scenario. Despite its significance, studies around the world indicate that much of the world's population still suffers from financial illiteracy and that measures to remedy the problem are urgently needed. This research focuses on millennials that are the biggest component of the labor force in Canada and as of 2012, households in Canada of the age group under 35 held over 824 billion in assets. Millennials are vulnerable to higher than average levels of disappointment due to their unrealistic financial goals. Despite of their decent earnings, they lack money management skills. The way they behave towards financial literacy may severely affect the economies and societies in which they choose to settle. This study finds out the factors motivating millennials to become financially educated and the best way to spread financial education among motivated people. Moreover, the focus of this research is to find out the sources that millennials get their financial information from. Also, the factors influencing the perception of financial literacy among low-income millennials in comparison to middle income group are studied. Based on our data analysis, we conclude that the overall perception of millennials that they are fairly knowledgeable seems to be a hindrance to their financial literacy. Financially empowered Canadians will reduce the burden on the social safety net and enable them to better plan for their own future. Though there are several programs launched by governments and other organizations in Canada, financial literacy is still a large scale problem. This research proposes future studies on financial literacy among youngsters and millennials so that timely action could be taken to prepare them for their future goals.
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 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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".