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Record W139784905 · doi:10.2202/1935-1704.1606

Status, Inequality and Intertemporal Choice

2010· article· en· W139784905 on OpenAlexaff
Bianjun Xia

Bibliographic record

VenueThe B E Journal of Theoretical Economics · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsEconomicsConsumption (sociology)Pareto principleInequalityExternalityElasticity of substitutionIntertemporal choiceEconometricsDistortion (music)MicroeconomicsDistribution (mathematics)Production (economics)MathematicsComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.238
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2010
Admission routes1
Has abstractyes

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