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Record W1556653765

Commentary on More on finance and growth: more finance, more growth?\\"

2003· article· en· W1556653765 on OpenAlexvenueno aff
Luigi Zingales

Bibliographic record

VenueCanadian parliamentary review · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsGross domestic productStock marketFinanceFinancial economicsMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

T en years ago the literature on the relation between finance and growth was set on its modern course by the publication of King and Levine’s (1993) influential paper. Much of the following work in this area was done by Ross Levine and his coauthors. Thus, none better than Levine himself could summarize the progress over the past decade, in the struggle to move from a correlation between financial development and economic development (Goldsmith, 1969) to establishing a causal relation between finance and growth. Levine emphasizes advances along two dimensions. First, in the measures of financial development. Goldsmith (1969) relied on the ratio of the value of financial intermediary assets to gross domestic product (GDP) as his only measure of financial development. Levine and coauthors have used many different variables, e.g., the liquid liability to GDP ratio, the credit in the private sector to GDP ratio, and the level of stock market turnover. Rajan and Zingales (1998) have even used the quality of accounting standards as a measure of a firm’s ability to raise funds. Nevertheless, as I will discuss momentarily, this first area is probably where less progress has been made. The second and more important dimension emphasized by Levine’s survey is in the attempt to establish causality. This is the area where most innovations have taken place. Their first step was to use the time dimension to identify the cause-effect relation (King and Levine, 1993), relying on the old “post hoc ergo propter hoc” argument. Levine and coauthors have subsequently enriched this approach using dynamic panel estimation, and further progress has been made in the use of instrumental variables (Rajan and Zingales, 1998, and Levine, 1998 and 1999). In both cases they use the La Porta et al. (1998) measures of legal origin as instrumental variables. I will discuss later whether and when these can be considered good instruments. A third step in trying to establish causality, which is not adequately surveyed by Levine, is the “natural experiment” approach. In a very clever paper, Jayaratne and Strahan (1996) use the banking deregulation across U.S. states as an exogenous change in financial development. This omission, justified on the basis of a decision not to focus on within-country studies, is the only shortcoming in Levine’s survey. Personally, I trust much more the natural experiment approach than the more sophisticated, but less robust, dynamic panel estimation techniques. The final step in the quest for a causal link, amply summarized by Levine, is to look in more detail at the mechanism through which finance spurs growth (see, e.g., Rajan and Zingales, 1998, and Demirguc-Kunt and Maksimovic, 1998). In spite of this minor quibble, Levine’s survey does an excellent job of summarizing the progress made in the past decade. In 1993 many people doubted that there was a relation between finance and growth; now very few do. Since Levine has documented so well what has been done, my role as a discussant is to describe what remains to be done. I will focus, thus, on the weak links in the quest for a reliable relation between finance and growth that policymakers can use in their decisions. I focus on six such weak links.

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)0.005
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.039
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.036
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0050.008
Scholarly communication0.0060.009
Open science0.0060.004
Research integrity0.0390.050
Insufficient payload (model declined to judge)0.0170.009

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 head0.022
GPT teacher head0.233
Teacher spread0.211 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations38
Published2003
Admission routes1
Has abstractyes

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