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Record W3124206738

Assessing the Efficiency Costs of Vietnam's 'Missing' Small and Medium Sized Enterprises: A Panel Data Investigation

2017· article· en· W3124206738 on OpenAlexaff
Trung Dang Le, Paul Shaffer

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsTrent University
Fundersnot available
KeywordsEquity (law)Panel dataBusinessMissing dataSkewScale (ratio)Small and medium-sized enterprisesDistribution (mathematics)Industrial organizationEconomicsFinanceEconometrics
DOInot available

Abstract

fetched live from OpenAlex

This article investigates whether there are efficiency costs associated with the pronounced rightward skew in the firm size distribution, or Vietnam's 'missing small and medium size enterprise (SMEs)', drawing on panel data analysis of firm growth and survival. Specifically, it examines if factor allocation biases with respect to credit, preferable treatment of state owned enterprises, barriers to entry into export markets and economies of scale are important determinants of growth rates and survival probabilities of small, medium and large-sized firms. Overall, findings on the earlier variables do not support the view that there are large efficiency costs associated with Vietnam's 'missing SMEs'. Together with other results in the literature with do not find significant equity costs associated with Vietnam's 'missing SMES', these findings raise questions about policy initiatives in support of SMEs in Vietnam, such as the National SME Support program, in particular, through improved access to credit.

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)0.007
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.072
GPT teacher head0.285
Teacher spread0.213 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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