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Record W1545237379 · doi:10.3386/w19276

Smart and Illicit: Who Becomes an Entrepreneur and Do They Earn More?

2013· report· en· W1545237379 on OpenAlexaff
Ross Levine, Yona Rubinstein

Bibliographic record

VenueNational Bureau of Economic Research · 2013
Typereport
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBusinessComputer securityInternet privacyMarketingAdvertisingComputer science

Abstract

fetched live from OpenAlex

We disaggregate the self-employed into incorporated and unincorporated to distinguish between "entrepreneurs" and other business owners. We show that the incorporated self-employed and their businesses engage in activities that demand comparatively strong nonroutine cognitive abilities, while the unincorporated and their firms perform tasks demanding relatively strong manual skills. The incorporated selfemployed have distinct cognitive and noncognitive traits. Besides tending to be white, male, and come from higher-income families, the incorporated-as teenagers-typically scored higher on learning aptitude tests, had greater self-esteem, and engaged in more disruptive, illicit activities. The combination of "smart" and "illicit" tendencies as youths accounts for both entry into entrepreneurship and the comparative earnings of entrepreneurs. In contrast to past research, we find that entrepreneurs earn more per hour and work more hours than their salaried and unincorporated counterparts.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.218
GPT teacher head0.443
Teacher spread0.226 · 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

Citations129
Published2013
Admission routes1
Has abstractyes

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