Bibliographic record
Abstract
Abstract Foundational research in marketing and behavioral economics has revealed a great deal about the psychology of budgeting. However, little is known about the extent to which budgets do (or do not) influence consumers’ real-world spending. The present research addresses this gap in the literature using naturally occurring budgeting and spending data provided by a popular personal finance app in the UK, a field experiment conducted with members of a Canadian credit union, and a financial diary study conducted with consumers in the US. Budget compliance is generally weak because budgets are wildly optimistic. However, optimistic budgets do help consumers reduce their spending. Moreover, the influence of budgets on spending is surprisingly sticky: consumers continue to reduce their spending six months after setting a budget, even though spending remains over-budget. Impulsive consumers exhibit worse budget compliance than less-impulsive consumers. However, counterintuitively, this is predominately because more impulsive consumers set lower budgets than less-impulsive consumers, not because they spend more. Finally, we provide evidence that budgets influence spending across several theory-informing psychographic variables. Taken together, these findings show that budgets can be both wildly optimistic and highly influential and that beliefs about the nature of consumers’ budgets require updating.
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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 | 0.002 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".