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Record W4247858770 · doi:10.1111/1468-0319.12179

Productivity – your countries need you!

2015· article· en· W4247858770 on OpenAlexaboutno aff

Bibliographic record

VenueEconomic Outlook · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityEconomicsEconomic stagnationLabour economicsFlexibility (engineering)Macroeconomics

Abstract

fetched live from OpenAlex

Increased global productivity could boost real wages, consumption, fiscal positions and alleviate fears of secular stagnation. But will it? Puzzles relate to the longer term global slowdown and to some countries' recent productivity‐less recoveries in jobs. We assess various explanations including mismeasurement, secular stagnation, financial sector malfunction and increased labour market flexibility. Our baseline forecast is for a moderate pro‐cyclical recovery in productivity but we show how downside risks imply it could be anaemic. Sustained weak productivity is a secular issue. Eight years after 2007, median productivity growth in OECD economies is less than Japan's was eight years into its lost decade. Aspects of secular stagnation and balance sheet adjustment have contributed. Measurement error may have played a role over the longer term. Recent experience divides recoveries into “haves” and “have nots” in terms of productivity and employment. The UK may finally be emerging from a “productivity‐less” recovery in employment after 2011; Spain and the Netherlands have experienced jobless recoveries in productivity; others, such as Canada and Sweden, have experienced pro‐cyclical (typically weak) recoveries in productivity; Italy hardly got going in either direction. Most theories provide, at best, a limited explanation for recent weak productivity performance. These include data mis‐measurement, increased labour market flexibility, financial sector malfunction and supply side secular stagnation. On balance, we think that a modest productivity bounce‐back could be imminent, caused by some demand recovery, tighter labour markets in major economies, higher real wages and firms deciding to invest more in capital, which enhances productivity and points the global economy towards normality. We also illustrate how global risk scenarios could dampen recovery. Negative skews imply mean G7 productivity growth across the scenarios would be an anaemic 1.1% in 2016, 0.5 percentage points (pp) lower than the baseline.

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)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.207
Threshold uncertainty score0.692

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0030.002
Scholarly communication0.0150.012
Open science0.0010.006
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.2070.175

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 head0.069
GPT teacher head0.326
Teacher spread0.256 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2015
Admission routes1
Has abstractyes

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