MétaCan
Menu
Back to cohort
Record W3027089670 · doi:10.1287/mnsc.2019.3545

Trust, Collaboration, and Economic Growth

2020· article· en· W3027089670 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

VenueManagement Science · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Policies and Impacts
Canadian institutionsMcGill University
Fundersnot available
KeywordsCommitInvestment (military)ProductivityEconomicsEx-anteIndustrial organizationValue (mathematics)Production (economics)MicroeconomicsCapital (architecture)Construct (python library)BusinessComputer scienceMacroeconomics

Abstract

fetched live from OpenAlex

We propose a macroeconomic model in which variation in the level of trust leads to higher innovation, investment, and productivity growth. The key feature in the model is a hold-up friction in the creation of new capital. Innovators generate ideas but are inefficient at implementing them into productive capital on their own. Firms can help innovators implement their ideas efficiently but cannot ex ante commit to compensating them appropriately. Rather, firms are disciplined only by the value of their reputations—the present value of their future partnerships. We model trust as a public signal and construct a correlated equilibrium. When trust is high, firms anticipate fruitful collaborations and thus can credibly commit to not expropriating inventors, leading to the more efficient production of new capital. Our model can be used to qualitatively replicate the empirical relation between measures of trust and investment, innovation, and productivity growth—at both the micro and macro level. This paper was accepted by Tomasz Piskorski, finance.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.793
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.024
GPT teacher head0.215
Teacher spread0.191 · 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