MétaCan
Menu
Back to cohort
Record W2767488613 · doi:10.3386/w24474

Financial Development, Growth, and Crisis: Is There a Trade-Off?

2018· report· en· W2767488613 on OpenAlexaff
Norman Loayza, Amine Ouazad, Romain Rancière

Bibliographic record

VenueNational Bureau of Economic Research · 2018
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsFinancial crisisFinancial systemBusinessEconomicsInternational economicsFinanceMacroeconomics

Abstract

fetched live from OpenAlex

This paper reviews the evolving literature that links financial development, financial crises, and economic growth in the past 20 years. The initial disconnect-with one literature focusing on the effect of financial deepening on long-run growth and another studying its impact on volatility and crisis-has given way to a more nuanced approach that analyzes the two phenomena in an integrated framework. The main finding of this literature is that financial deepening leads to a trade-off between higher economic growth and higher crisis risk; and its main conclusion is that, for at least middle-income countries, the positive growth effects outweigh the negative crisis risk impact. This balanced view has been revisited recently for advanced economies, where an emerging and controversial literature supports the notion of "too much finance," suggesting that there might be a threshold beyond which financial depth becomes detrimental for economic growth by crowding out other productive activities and misallocating resources. Nevertheless, the growth/crisis trade-off is alive and strong for a large share of the world economy. Recognizing the intrinsic trade-offs of financial development can provide a useful framework to design policies targeting financial deepening, diversity, and inclusion. In particular, acknowledging the trade-offs can highlight the need for complementary policies to mitigate the risks, from financial macroprudential policies to monetary policy frameworks that monitor the growth of credit and asset prices.

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 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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.270
GPT teacher head0.435
Teacher spread0.165 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations19
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueNational Bureau of Economic ResearchSame topicGlobal Financial Crisis and PoliciesFrench-language works237,207