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Record W3126024327

The Role of the Structural Transformation in Aggregate Productivity

2007· preprint· en· W3126024327 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRePEc: Research Papers in Economics · 2007
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProductivityEconomicsAgricultureAgricultural productivityLabour economicsAggregate (composite)Multifactor productivityEconomic geographyTotal factor productivityMacroeconomicsGeography
DOInot available

Abstract

fetched live from OpenAlex

We investigate the role of sectoral differences in labor productivity in explaining the process of structural transformation – the secular reallocation of labor across sectors – and the time path of aggregate productivity across countries. Using a simple model of the structural transformation that is calibrated to the growth experience of the United States, we measure sectoral labor productivity differences across countries. Productivity differences between rich and poor countries are large in agriculture and services and smaller in manufacturing. Moreover, over time, productivity gaps have been substantially reduced in agriculture and industry but not nearly as much in services. In the model, these sectoral productivity patterns generate implications that are broadly consistent with the cross-country evidence on the structural transformation, aggregate productivity paths, and relative prices. We show that productivity catch-up in industry explains about 50 percent of the gains in aggregate productivity across countries, while low relative productivity in services and the lack of catch-up explains all the experiences of slowdown, stagnation, and decline observed across countries.

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.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.268
Teacher spread0.241 · 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