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Record W2119951153 · doi:10.3386/w14273

Racial Discrimination and Competition

2008· report· en· W2119951153 on OpenAlexaboutno aff
Ross Levine, Alexey Levkov, Yona Rubinstein

Bibliographic record

VenueNational Bureau of Economic Research · 2008
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsnot available
FundersHarvard UniversityBrown University
KeywordsCompetition (biology)WageDeregulationLabour economicsEconomicsQuarter (Canadian coin)Prejudice (legal term)BusinessMarket economyPolitical science

Abstract

fetched live from OpenAlex

This paper assesses the impact of competition on racial discrimination. The dismantling of inter-and intrastate bank restrictions by U.S. states from the mid-1970s to the mid-1990s reduced financial market imperfections, lowered entry barriers facing nonfinancial firms, and boosted the rate of new firm formation. We use bank deregulation to identify an exogenous intensification of competition in the nonfinancial sector, and evaluate its impact on the racial wage gap, which is that component of the black-white wage differential unexplained by Mincerian characteristics. We find that bank deregulation reduced the racial wage gap by spurring the entry of non-financial firms. Consistent with taste-based theories, competition reduced both the racial wage gap and racial segregation in the workplace, particularly in states with a comparatively high degree of racial prejudice, where competition-enhancing bank deregulation eliminated about one-quarter of the racial wage gap after five years.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.001

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 head0.374
GPT teacher head0.462
Teacher spread0.088 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations53
Published2008
Admission routes1
Has abstractyes

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