The Relationship between Budgeting and Indicators of Financial Well-Being
Bibliographic record
Abstract
Financial well-being is an emerging topic among financial literacy practitioners. Financial well-being is linked to financial behaviors such as day-to-day money management, and planning and saving for the future, both of which are influenced by an individual's ability to apply their financial knowledge and skills in order to manage their expenses. Such courses of action can be linked to the development of a budget. It is the goal of this research to explore the role that budgeting, in addition to further financial behaviors, has on the achievement of financial well-being outcomes. An analysis of the 2014 Canadian Financial Capability Survey was used to explore the relationship between how budgeting and financial behaviors impact indicators of financial well-being. This analysis finds always staying within a household budget helps individuals control their spending by imposing restraints while also encouraging savings practices. Together, this aids individuals in achieving positive financial well-being outcomes. The findings of this paper also suggest that individuals who partake in budgeting practices do so because they are facing constraint. However, this constraint affects the probability an individual will always stay within their household budget. Finally, this paper finds that other predictors of financial well-being are financial confidence, financial knowledge, and impulsivity.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".