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Record W3083073839 · doi:10.5430/rwe.v11n5p259

Determinants of Profitability in Micro-enterprises Incorporated by Migrants in“Mercardo Artesanal”, Guayaquil-Ecuador

2020· article· en· W3083073839 on OpenAlexvenueno aff
Maria Belen Bravo Avalos, Maritza Lucia Vaca Cardenas, Gisella Nicole Miño Montero

Bibliographic record

VenueResearch in World Economy · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsProfitability indexIndex (typography)BusinessSocioeconomic statusComposite indexComposite indicatorFinancePopulationFinancial systemSociology

Abstract

fetched live from OpenAlex

The main objective of the study was to examine the effects on profitability of socioeconomic factors and business management skills of migrant micro-entrepreneurs. Our results are based on 162 surveys conducted with migrant businesspeople at Mercado Artesanal, in Guayaquil-Ecuador, between February 18-22, 2020. We applied a modified composite index developed in 2016 by Bell for evaluating entrepreneurial skills of Otavaleño migrants, who own micro-enterprises at Mercado Artesanal. We found that over 81% of the microfirms operated by migrants in the examined marketplace are profitable. Differences in profitability between migrant and resident micro-entrepreneurs are also discussed. Like prior study findings our results suggest that migrant micro-entrepreneurs show greater business performance compared to local businessmen. In addition, a straightforward profitability index, SPI, was calculated for assessing the determinants of profitability of local and migrant micro-entrepreneurs. Authors believe that the proposed index should be useful mainly for micro-enterprises and market sellers, where the customary measurement methods cannot be applied.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.332
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.089
GPT teacher head0.381
Teacher spread0.293 · 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.

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
Published2020
Admission routes1
Has abstractyes

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