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Record W2767795030

Immigrant Entrepreneurship: Trends and Contributions

2017· article· en· W2767795030 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCato Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationEntrepreneurshipProductivityHuman capitalEconomicsPovertyLabour economicsEconomic growthBusinessPolitical scienceFinanceLaw
DOInot available

Abstract

fetched live from 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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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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
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 studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.326
Teacher spread0.299 · 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