Bibliographic record
Abstract
This paper develops an intertemporal model in which individuals care about consumption not only for its own sake, but also for the status it implies. By putting an additive status term into the utility function, I show that the level of inequality in the initial wealth distribution affects individuals saving and consumption behavior. The direction of the distortion in intertemporal choice relative to the standard model without a concern for status depends on the elasticity of intertemporal substitution in the utility from absolute consumption. In particular, I prove that, for conventional parameter values of the elasticity (e.g. CES parameter larger than one), people save less than what they do without the status concern but the magnitude of this decrease is reduced by the concern for future status. It is also possible that people save more than what they do without the status concern. I also analyze how changes in the initial wealth distribution affect saving. For example, when wealth is Pareto distributed, for a reasonable parameterization, the rich save more and the poor save less when society gets more unequal, which implies that inequality is self-enforcing in this economy. Finally, the resulting allocation is Pareto inefficient due to the externalities generated by the concern for status.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".