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Record W2764200494

2017-11 Different Paths? Human Capital Prices, Wages and Inequality in Canada and the US

2017· preprint· en· W2764200494 on OpenAlexaboutno aff
Audra J. Bowlus, Chris Robinson

Bibliographic record

VenueEconstor (Econstor) · 2017
Typepreprint
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityEconomicsHuman capitalLabour economicsInequalityPer capitaCapital (architecture)Capital deepeningRelative priceDemographic economicsMonetary economicsCapital formationMacroeconomicsFinancial capitalEconomic growthPopulation
DOInot available

Abstract

fetched live from OpenAlex

In the last three decades, Canada and the US showed different paths in per capita GDP growth, skill premiums and inequality. Both firm and worker productivity differences play a role and have different policy implications, but are difficult to distinguish. To examine separate firm and worker productivity effects, human capital prices and quantities are estimated using the methods developed in Bowlus and Robinson (2012). The quantities reflect worker productivity while the prices tend to reflect firm productivity. In the US there was faster growth and a much more rapid rise in skill premia and inequality. This was primarily due to different paths for the relative price paid to rent high skilled human capital in the two countries, rather than differences in relative quantities of human capital supplied by the typical high skilled worker. Worker productivity increased for high skilled workers, but decreased for low skilled workers over the 1980-2000 period.

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)0.003
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 score0.051
Threshold uncertainty score0.369

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.273
Teacher spread0.248 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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