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Immigrant Entrepreneurship: Trends and Contributions

2017· article· en· W2767795030 sur OpenAlex

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Notice bibliographique

RevueCato Journal · 2017
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueMigration, Ethnicity, and Economy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésImmigrationEntrepreneurshipProductivityHuman capitalEconomicsPovertyLabour economicsEconomic growthBusinessPolitical scienceFinanceLaw
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Edward Lazear, Stanford economics professor and former chairman of the President's Council of Economic Advisers, has said, entrepreneur is the single most important player in a modern economy (Lazear 2002: 1; see also Holcombe 1998). The emphasis on entrepreneurship and small and startup businesses as key engines in job creation, innovation, and economic growth has a long history, going back to Adam Smith. Not surprisingly, governments around the world view promoting entrepreneurship as a national and local priority. The interest is driven primarily by evidence that small and young businesses create a disproportionate share of new jobs in the economy, represent an important source of innovation, increase national productivity, and alleviate poverty (Reynolds 2005, OECD 2005, U.S. Small Business Administration 2011, Decker et al. 2014). A frequently held view, often supported by research, is that immigrants are especially entrepreneurial, a sentiment commonly shared by policymakers and reflected in immigration policies. Many developed countries, including the United States, have created special visas and entry requirements in an attempt to attract immigrant entrepreneurs (Fairlie and Lofstrom 2015). In addition to possible contributions to economic growth, employment, and innovation, immigrant business ownership may also act as a tool to enhance immigrant labor market integration and success (Cummings 1980). For example, self-employment may alleviate informational gaps regarding education, skills, and experience gained by immigrants in their home countries, where U.S. employers are uncertain about how foreign-obtained human capital relates to productivity in the United States. Labor market discrimination and limited English proficiency are other possibly relevant hurdles faced more by immigrants than by U.S.-born workers. Although relevant across all levels of skills, this arguably may be most relevant to low-skilled immigrants, who face the highest hurdles to hiring in an increasingly skill-intensive economy. This article analyzes recent U.S. data to examine how immigrants during the last 15 years have contributed to entrepreneurship through self-employment and earnings. It aims to address the questions of how do immigrants contribute to recent U.S. selfemployment trends, in what industries are immigrant entrepreneurs concentrated, and how do their earnings compare to those of U.S.-born entrepreneurs? Before turning to the analysis, aimed to provide a broader understanding of contributions of immigrants to entrepreneurship, I begin with a brief overview of the relevant literature. (1) Literature Review of Immigrant Entrepreneurship There are a number of relevant ways to measure entrepreneurship and immigrant contributions to it. One is by measuring business ownership and startups. A body of research has consistently found that business ownership is higher among the foreign-born than the native-born in many developed countries such as the United States, United Kingdom, Canada, and Australia (Borjas 1986; Lofstrom 2002; Clark and Drinkwater 2000, 2010; Schuetze and Antecol 2007; Fairlie et al. 2010). Immigrants in the United States are also found to be more likely to start businesses than the native born (Fairlie 2008). Other measures of entrepreneurship also point toward significant immigrant contributions. For example, as a recent review of the relevant literature shows, immigrants are greatly over-represented among U.S.-based Nobel Prize winners, high-impact companies, patent applications, and members of the National Academy of Sciences and the National Academy of Engineering (Fairlie and Lofstrom 2015). They are also over-represented among founders of high-tech companies, biotech firms, biotech companies undergoing initial public offerings, and public venture-backed U.S. companies. Nonetheless, some researchers urge caution about interpreting immigrant contributions to the high-tech sector of the economy. …

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Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,322
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0030,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,027
Tête enseignante GPT0,326
Écart entre enseignants0,299 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle