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Record W3124239262 · doi:10.7249/wr727

Indian Entrepreneurial Success in the United States, Canada and the United Kingdom

2010· book· en· W3124239262 on OpenAlexaboutno aff
Robert W. Fairlie, Harry Krashinsky, Julie Zissimopoulos, Krishna B. Kumar

Bibliographic record

VenueRAND Corporation eBooks · 2010
Typebook
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
FundersNational Institute on AgingEwing Marion Kauffman Foundation
KeywordsImmigrationKingdomCensusEarningsEntrepreneurshipEconomic growthPolitical scienceDemographic economicsBusinessEconomicsDemographySociologyPopulationAccounting

Abstract

fetched live from OpenAlex

Indian immigrants in the United States and other wealthy countries are successful in entrepreneurship.Using Census data from the three largest developed countries receiving Indian immigrants in the world --the United States, United Kingdom and Canada --we examine the performance of Indian entrepreneurs and the causes of their success.We find that in the United States Indian entrepreneurs have average business income that is substantially higher than the national average and is higher than any other immigrant group.High levels of education among Indian immigrants in the United States are responsible for nearly half of the higher level of entrepreneurial earnings while industry differences explain an additional 10 percent.In Canada, Indian entrepreneurs have average earnings slightly below the national average but they are more likely to hire employees, as are their counterparts in the United States and United Kingdom.The Indian educational advantage is smaller in Canada and the United Kingdom contributing less to their entrepreneurial success.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.590

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.246
Teacher spread0.222 · 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 designNot applicable
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
Published2010
Admission routes1
Has abstractyes

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