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Record W3122062156 · doi:10.1142/s201013921250019x

The Dynamics of Geographic versus Sectoral Diversification: Is There a Link to the Real Economy?

2012· preprint· en· W3122062156 on OpenAlex
Francesca Carrieri, Vihang R. Errunza, Sergei Sarkissian

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

VenueQuarterly Journal of Finance · 2012
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsMcGill University
Fundersnot available
KeywordsDiversification (marketing strategy)Equity (law)EconomicsProduction (economics)Emerging marketsEconomic geographyIndustrial productionMonetary economicsBusinessMacroeconomics

Abstract

fetched live from OpenAlex

We study the dynamics of gains from sectoral versus geographic diversification and relate economic sources to changes in those gains. We estimate conditional correlations between returns on the US equity market and 16 equity markets and 10 local industries from other OECD countries and find that the average correlation across countries has increased in relation to that across industries. We also show that this process is accompanied by increased alignment in the industrial structures across countries and an increase in the average conditional correlation of aggregate production growth across countries relative to that of disaggregated production growth, especially among developed economies. Thus, the increased benefits of industry-level investing across developed markets are reflected in the real side of the global economy. However, country-level investing should remain the predominant asset allocation approach in emerging markets.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.033
GPT teacher head0.229
Teacher spread0.196 · 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