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Record W4214505509 · doi:10.47872/laer.v31.32

Size-Dependent Gender Gaps in Entrepreneurship: The Case of Chile*

2022· article· en· W4214505509 on OpenAlexaboutno aff
David Cuberes, Marc Teignier

Bibliographic record

VenueLatin American Economic Review · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
FundersMinisterio de Economía y CompetitividadInter-American Development Bank
KeywordsEntrepreneurshipProductivityAggregate (composite)Demographic economicsQuarter (Canadian coin)EconomicsLabour economicsAggregate incomeValue (mathematics)Gender gapBusinessEconomic growthIncome distributionFinanceGeographyInequalityStatistics

Abstract

fetched live from OpenAlex

This paper documents differences in firm size depending on whether their manager is a man or a woman and studies the aggregate implications of these gender gaps in Chile. We document that in 2007 less than a quarter of firms are managed by women and that this gap takes its largest value for managers with tertiary education or more. In terms of their number of workers, female-run firms are on average about three times smaller than those run by men. Moreover, the ratio of men to women managers is always above one, but it is much higher for large and medium firms than for small or micro ones. These differences remain significant after controlling for several manager and firm characteristics. We then use an extended version of the theoretical framework developed in Cuberes and Teignier (2016) to incorporate these facts and obtain quantitative predictions about their effects on aggregate productivity and income in Chile. We find that the observed gender gaps in entrepreneurship in Chile generate a fall in aggregate productivity and aggregate income of 7.5%.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.254
Teacher spread0.233 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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