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Record W3121245831 · doi:10.1920/wp.ifs.2004.0415

Changes in the world distribution of output-per-worker 1960-98: how a standard decomposition tells an unorthodox story

2017· paratext· en· W3121245831 on OpenAlexaff
Paul Beaudry, Fabrice Collard, David A. Green

Bibliographic record

Venuenot available
Typeparatext
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsUniversity of British ColumbiaBishop's University
Fundersnot available
KeywordsDecompositionDistribution (mathematics)StatisticsMathematicsEconometricsBiologyMathematical analysisEcology

Abstract

fetched live from OpenAlex

Why have some countries done so much better than others over the recent past? In order to shed new light on this issue, this paper provides a decomposition of the change in the distribution of output-per-worker across countries over the period 1960-98. The main finding of the paper is that most of the change in shape of the world distribution of income between 1960-1998 can be accounted for by a very substantial and previously unrecognized change in the parameters driving the growth process. In particular, we show that the role of capital deepening forces - that is the role of investment rates and population growth in affecting output - increased dramatically over the period 1978-98 versus 1960-78, and that this increase can account for almost all the observed changes in the world distribution. In contrast, we do not find any significant effects coming through non-linear convergence mechanisms or increased importance of education; both of which have played prominent roles in recent discussion of economic performance. Our results therefore highlight that the period 1978-98 was particularly advantageous to countries which strongly favored capital accumulation and hence suggests that research aimed at understanding recent differences in economic performances across countries needs to focus on explaining why the social returns to physical capital accumulation where abnormally high over the period 1978-98.

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 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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.536
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.050
GPT teacher head0.271
Teacher spread0.221 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations5
Published2017
Admission routes1
Has abstractyes

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