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Record W4225727761 · doi:10.1093/ser/mwac036

Ups and downs in finance, ups without downs in inequality

2022· article· en· W4225727761 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocio-Economic Review · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsStatistics Canada
FundersEuropean Social FundAgence Nationale de la RechercheNorges ForskningsrådForskningsrådet om Hälsa, Arbetsliv och VälfärdMinisterio de Ciencia e InnovaciónDanmarks Frie ForskningsfondNational Science Foundation
KeywordsInequalityEconomicsRestructuringEarningsCapital (architecture)Labour economicsEconomic inequalityFinanceMonetary economics

Abstract

fetched live from OpenAlex

Abstract The upswing in finance in recent decades has led to rising inequality, but do downswings in finance lead to a symmetric decline in inequality? We analyze the asymmetry of the effect of ups and downs in finance, and the effect of increased capital requirements and the bonus cap on national earnings inequality. We use administrative employer–employee-linked data from 1990 to 2019 for 12 countries and data from bank reports, from 2009 to 2017 in 13 European countries. We find a strong asymmetry in the effect of upswings and downswings in finance on earnings inequality, a weak, if any, mitigating effect of capital requirements on finance’s contribution to inequality, and a restructuring but no absolute effect of the bonus cap on financiers’ earnings. We suggest that while rising financiers’ wages increase inequality in upswings, they are resilient in downswings and thus downswings do not contribute to a symmetric decline in inequality.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.039
GPT teacher head0.266
Teacher spread0.227 · 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