Bibliographic record
Abstract
Abstract Household Finance: An Introduction to Individual Financial Behavior is about how individuals make financial decisions and how these financial decisions contribute to and detract from their well-being. Financial decision makers must plan, save, take on an appropriate amount of risk, insure assets when needed, handle debt appropriately, and invest, either on their own or through delegating portfolio management. These and other decisions are covered, both in the normative sense (i.e., what is best) based on conventional financial theory and in the positive sense (i.e., what is actually done) based on observing behavior. Household finance thus covers both modern finance and behavioral finance at the level of the household decision-making unit. While modern finance builds models of behavior and markets based on strong assumptions such as the rationality of decision makers, behavioral finance is based on the view that sometimes people behave in a less-than-fully-rational fashion when making financial decisions. Important puzzles and issues are addressed, such as financial illiteracy, whether education and advice can improve outcomes, intertemporal consumption optimization, consumption smoothing, optimal dynamic risk-taking, the stock market participation puzzle, the credit card debt puzzle, anomalous insurance decisions, mortgage choices, skewness preference, investments driven by availability and attention, local and home bias, the disposition effect, optimal pension design, and improving outcomes through nudging.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.212 | 0.120 |
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".