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Record W1991980751 · doi:10.1162/0033553041502171

Investor Protection, Optimal Incentives, and Economic Growth

2004· article· en· W1991980751 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

VenueThe Quarterly Journal of Economics · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSternIncentiveManagementPolitical scienceSociologyLibrary scienceLaw and economicsEconomicsHistoryComputer science

Abstract

fetched live from OpenAlex

Does investor protection foster economic growth? To assess the widely held affirmative view, we introduce investor protection into a standard overlapping generations model of capital accumulation. Better investor protection implies better risk sharing. Because of entrepreneurs' risk aversion, this results in a larger demand for capital. This is the demand effect. A second effect (the supply effect) follows from general equilibrium restrictions. Better protection (i.e., higher demand) increases the interest rate and lowers the income of entrepreneurs, decreasing current savings and next period's supply of capital. The supply effect is stronger the tighter are the restrictions on capital flows. Our model thus predicts that the (positive) effect of investor protection on growth is stronger for countries with lower restrictions. Cross-country data provide support for this prediction, as does the detailed examination of the growth experiences of South Korea and India.

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 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 score0.323
Threshold uncertainty score0.328

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 communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.179
Teacher spread0.164 · 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