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Record W3122897537

Legal Institutions, Sectoral Heterogeneity, and Economic Development

2006· preprint· en· W3122897537 on OpenAlexafffund
Rui Castro, Gian Luca Clementi, Glenn MacDonald

Bibliographic record

VenueThe Faculty Digital Archive (New York University) · 2006
Typepreprint
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of CanadaUniversité de Montréal
KeywordsInvestment goodsInvestment (military)Capital goodEconomicsRelative priceMonetary economicsConsumption (sociology)Capital (architecture)Total factor productivityReturn on investmentInternational economicsMarket economyProductivityMacroeconomicsGoods and servicesBusiness cycleProduction (economics)
DOInot available

Abstract

fetched live from OpenAlex

(Download the most recent version) Poor countries have lower PPP–adjusted investment rates and face higher relative prices of investment goods. It has been suggested that this happens either because these countries have a relatively lower TFP in industries producing capital goods, or because they are subject to greater investment distortions. This paper provides a micro–foundation for the cross–country dispersion in investment distortions. We first document that firms producing capital goods face a higher level of idiosyncratic risk than their counterparts producing consumption goods. In a model of capital accumulation where the protection of investors ’ rights is incomplete, this difference in risk induces a wedge between the returns on investment in the two sectors. The wedge is bigger, the poorer the investor protection. In turn, this implies that countries endowed with weaker institutions face higher relative prices of investment goods, invest a lower fraction of their income, and end up being poorer. We find that our mechanism may be quantitatively important.

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)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0370.001

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 head0.037
GPT teacher head0.219
Teacher spread0.181 · 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

Citations2
Published2006
Admission routes2
Has abstractyes

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