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Record W2094111477 · doi:10.1257/aer.102.1.556

The Internet and Local Wages: A Puzzle

2012· article· en· W2094111477 on OpenAlexaff
Chris Forman, Avi Goldfarb, Shane Greenstein

Bibliographic record

VenueAmerican Economic Review · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThe InternetWage growthWageEconomicsInvestment (military)Wage inequalityLabour economicsEfficiency wagePolitical science

Abstract

fetched live from OpenAlex

How did the diffusion of the internet affect regional wage inequality? We examine the relationship between business investment in advanced internet technology and local variation in US wage growth between 1995 and 2000. We identify a puzzle. The internet is widespread, but the economic payoffs are not. Advanced internet technology is only associated with substantial wage growth in the 6 percent of counties that were already highly wealthy, educated, and populated and had IT-intensive industry. Advanced internet and wage growth appear unrelated elsewhere. Overall, advanced internet explains over half the difference in wage growth between already well-off counties and all others. JEL: J31, L86, O33, R11, R23

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.235
Teacher spread0.211 · 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 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

Citations428
Published2012
Admission routes1
Has abstractyes

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