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Record W2767795030

Immigrant Entrepreneurship: Trends and Contributions

2017· article· en· W2767795030 on OpenAlexaboutno aff
Magnus Lofstrom

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. …

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.008
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.008
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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

Citations12
Published2017
Admission routes1
Has abstractyes

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