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Record W2977678163

Self-selection, location, and entrepreneurship: British self-employment in North America in the early 20th century

2005· preprint· en· W2977678163 on OpenAlexaboutno aff
Chris Minns, Marian Rizov

Bibliographic record

VenueRePEc: Research Papers in Economics · 2005
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationEarningsEntrepreneurshipEconomicsDemographic economicsLabour economicsEconomyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

"The decision to undertake international migration has long interested economics and economic historians alike. Labour economists have placed considerable emphasis to the nature of self-selection among immigrants: under what conditions do individuals with high or low levels of skill choose to relocate across borders? Economic theory has been used to relate immigrant self-selection to relative economic conditions between source and destination regions, with the relative dispersion of earnings in the two areas thought to be a key determinant of immigrant selection (Borjas, 1987). This theoretical perspective can be extended to consider the choices made by immigrants from a single source country among a wider set of potential destination markets. While labour economists have examined immigrant selectivity in contemporary labour markets, the early twentieth century offers a better testing ground for this theory. As flows between Europe and North America were largely unregulated prior to 1917, we can observe how immigrants self-select across a set of labour markets in the absence of rigid immigrant policies and other constraints. Can differences in economic conditions lead destination countries to receive immigrants from the same source but with very different abilities in the labour market? Scholars of nineteenth century North America have asked this very question with reference to British immigration. Some observers of British immigration claimed that, due to the limited range of labour market opportunities in Canada, Britons choosing to reside in this country were considerably less able than those who chose to move to the United States. Recent work by Green, MacKinnon, and Minns (2002) finds that the economic characteristics of British immigrants in both countries were broadly similar circa 1900, but self-selection may lead to immigrant populations with important differences in unmeasured characteristics, such as schooling or innate ability. In this paper, we search for evidence of differences in immigrant self-selection across destination choices through an assessment of self-employment among British immigrants in Canada and the United States in the early twentieth century. We examine self-employment because it is one of the few indicators of labour market status available in both Canadian and American Census data from the early twentieth century, and one that is associated with high levels of human capital (Minns and Rizov, 2003). We use the United States Census of 1910 and the Census of Canada of 1901 to estimate the determinants of self-employment in both countries. Our results will provide important evidence on possible differences in immigrant self-selection between the two labour markets: in either the United States or Canada, were British immigrants better endowed with the characteristics associated with successful entrepreneurship, or more likely to become self-employed entrepreneurs, conditional on observable characteristics?"

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.636

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.002
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.294
Teacher spread0.275 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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