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Record W4313365186 · doi:10.1257/app.20200807

The Effect of Immigration Restrictions on Local Labor Markets: Lessons from the 1920s Border Closure

2022· article· en· W4313365186 on OpenAlex
Ran Abramitzky, Philipp Ager, Leah Platt Boustan, Elior Cohen, Casper Worm Hansen

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

VenueAmerican Economic Journal Applied Economics · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationClosure (psychology)Labour economicsEconomicsDemographic economicsPolitical scienceDevelopment economicsMarket economy

Abstract

fetched live from OpenAlex

In the 1920s, the United States substantially reduced immigration by imposing country-specific entry quotas. We compare local labor markets differentially exposed to the quotas due to variation in the national-origin mix of their immigrant population. US-born workers in areas losing immigrants did not benefit relative to workers in less exposed areas. Instead, in urban areas, European immigrants were replaced with internal migrants and immigrants from Mexico and Canada. By contrast, farmers shifted toward capital-intensive agriculture, and the immigrant-intensive mining industry contracted. These differences highlight the uneven effects of the quota system at the local level. (JEL J15, J18, J31, K37, N32, N42, R23)

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.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.262
Teacher spread0.257 · 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