MétaCan
Menu
Back to cohort

COSTLY INTERMEDIATION AND THE POVERTY OF NATIONS*

2007· preprint· en· W3121308030 on OpenAlexaff
Shankha Chakraborty, Amartya Lahiri

Bibliographic record

VenueInternational Economic Review · 2007
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEconomicsPer capita incomeIncome distributionInvestment (military)PovertyDistribution (mathematics)Public capitalDeveloping countryIntermediationSample (material)Financial intermediaryDistortion (music)Monetary economicsMacroeconomicsPublic investmentInequalityEconomic growthFiscal policy

Abstract

fetched live from OpenAlex

This article has two goals: (i) to reduce the 7‐fold productivity differential required to explain the observed 33‐fold income difference between the richest and poorest countries of the world; and (ii) to explain cross‐country differences in the capital‐output ratio. To achieve the first goal we modify the production function of the standard neoclassical growth model to include public capital whose provision is subject to intermediation costs. For the second goal we distort private investment by introducing credit frictions. The model, quantified using cross‐country data, generates an income gap of 33 with productivity differences of only 3 under the measured variations in public and private capital. The required productivity gap declines even further, to 2.1, when we introduce a home‐production sector. On the second goal, however, credit frictions do a poor job of explaining cross‐country variations in the capital‐output ratio.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.039
GPT teacher head0.279
Teacher spread0.240 · 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 designTheoretical or conceptual
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

Citations0
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueInternational Economic ReviewSame topicFiscal Policy and Economic GrowthFrench-language works237,207