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Record W2102134654 · doi:10.25336/p63w4d

Income inequality, status seeking, and savings rates in Canada

2014· article· en· W2102134654 on OpenAlexvenueaboutno aff
Alexander Bilson Darku

Bibliographic record

VenueCanadian Studies in Population · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsEconomic inequalityConsumption (sociology)Demographic economicsInequalityPersonal incomePer capitaPer capita incomeTotal personal incomeConsumer Expenditure SurveyIncome inequality metricsIncome distributionLabour economicsPublic economicsEconomic growthDemographyPopulationGross incomeAggregate expenditureSociology

Abstract

fetched live from OpenAlex

This paper uses Canadian provincial-level data and a variant of James uesenberry’s relative income hypothesis proposed by Frank et al. (2010) to examine the relationship between income inequality and savings rates. The theory predicts that increased expenditure of top income earners leads those just below them in the income scale to spend more as well, then the next group also spends more, and so on. This phenomenon is due to people’s status seeking behaviour. Hence, increased income inequality will trigger increases in consumption by individuals in all income groups, which in turn leads to declining personal savings rates. The empirical analysis based on this theory led to some interesting findings. First, at the national level, increased income inequality has a significant negative effect on personal savings rates. At the provincial level, the relationship also emerges in eight of ten provinces. Second, both the national and provincial results imply that growth in per capita income that worsens income inequality impacts negatively on personal savings rates. I interpret the results as evidence that social factors such as status-seeking generate consumption interdependence and are significant determinants of consumption and savings decisions of Canadians.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.048
GPT teacher head0.340
Teacher spread0.292 · 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 designObservational
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

Citations13
Published2014
Admission routes2
Has abstractyes

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