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Record W1585326651

Fiscal harmonization and migration in the European Union

2006· article· en· W1585326651 on OpenAlexaboutno aff
Socrates Karidis, Michael A. Quinn

Bibliographic record

VenueBrussels economic review · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsTiebout modelHarmonizationEconomicsEuropean unionContext (archaeology)Maastricht TreatyInternational economicsImmigrationTax harmonizationTreatyPublic economicsMacroeconomicsEuropean integrationIndirect taxTax competitionTax reformPolitical sciencePublic goodGeography
DOInot available

Abstract

fetched live from OpenAlex

The focus of this paper is the impact of fiscal policies on international migration flows. The Tiebout hypothesis proposes that individuals consider differences in tax rates and social spending when making migration decisions. While evidence of the Tiebout hypothesis has been found in several domestic U.S. and Canadian studies, this is the first paper to test the Tiebout hypothesis using bilateral international migration flows. The Maastricht treaty has created a unique opportunity to study migration in an international context by removing legal barriers to migration within the European Union. Using data from EU countries throughout the 1980s and 1990s, this paper finds significant statistical support for the Tiebout hypothesis with regards to international migration flows. These results suggest that achieving greater fiscal harmonization across countries would lower migration flows. The results also imply that EU countries which are resistant to achieving fiscal harmonization with members may, as a result, have problems in attaining their other goal of reducing immigration (inward) from these countries.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.221
Teacher spread0.192 · 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

Citations6
Published2006
Admission routes1
Has abstractyes

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