Bibliographic record
Abstract
This study examines the link between consumer expenditures and the Conference Board's Index of Consumer Attitudes, an index highly regarded for some time as a useful leading indicator of consumer expenditures. However, the theory that identifies why it may be useful in an analysis of consumption is less well established. To explore this question, we investigate the complementary value of including the index in a consumption equation. We also take a closer look at the index to establish what information it captures and why it may be useful in explaining household expenditures. The results suggest that the consumer attitudes index supplements traditional economic variables such as real income, wealth, interest rates, and the unemployment rate in equations explaining household expenditures. This finding is quite robust. Furthermore, when tested separately in the equation, the individual questions that contribute most to the index's explanatory power are those on current income as well as the "good time to buy" question that likely reflects consumers' assessments of their economic environment. The results suggest that the attitudes index partially captures information about expected income but that its explanatory power may also come from its measurement of the perception of economic prospects, including some assessment of the probability of a negative outcome and the uncertainty of economic prospects.
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 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".