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Should the Brain Drain Be Plugged? A Behavioral Economics Approach

2004· article· en· W233861818 sur OpenAlexaboutno aff
Lisa Leiman

Notice bibliographique

RevueTexas international law journal · 2004
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueMigration and Labor Dynamics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEmigrationImmigrationWageProtectionismBrain drainDeveloping countryPoliticsEconomicsPolitical scienceDevelopment economicsLabour economicsDemographic economicsPolitical economyEconomic growthInternational economicsLaw
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

I. INTRODUCTION For decades, highly-skilled and educated workers have been immigrating to the United States and other developed countries for various reasons, including higher earning potentials, greater ability to find skillset-appropriate jobs, and improved political and social stability. Many writers have extolled the virtues of open immigration policies and free movement of skilled workers1-often arguing against protectionist immigration barriers, citing studies that indicate a positive economic impact on receiving countries and individual immigrants.2 Other academics have chosen to classify movement of workers among countries as circulation.3 But in many scholars' views, the emigration of educated workers from poorer regions to wealthier ones more accurately viewed as a drain, a term used to refer to the exodus of the brightest, most skilled, and most productive members of a society.4 Such migrations may not elevate total world output, since the individual's private calculation of the gain from emigrating does not take into account certain social costs, especially on the country of emigration, that the move may bring about.5 While brain drain can affect any country,6 this paper concerns the situation in lessdeveloped countries (LDCs) and contends that the movement of trained and educated workers from the developing to the developed world has harmful effects that are not sufficiently counteracted by the theoretical efficiency of allowing workers to move to places where their skills are valued at higher wage rates and where they can realize higher returns on educational investments. The conventional economic analysis that free movement of workers generates no net losses seems untenable through a behavioral economics analysis, because educated workers contribute at different levels based on the extent to which their country developed. In addition, because education requires an upfront investment, a brain drain can severely limit a country's incentive to invest in human capital that it expects might ultimately flee. This paper further examines some of the behavioral and economic forces that provide incentives for workers to leave their native developing countries and take their skills to developed countries. Most importantly, it focuses on ways to limit a brain drain per se and on potential solutions to the problems in LDCs that arise from emigration of the most highly-skilled and educated citizens. In this analysis, it critical to recognize the individuals' behavioral tendencies and cognitive biases that might ultimately generate undesired responses to the proposed solutions. II. WHO IS INVOLVED? A. Receiving Countries The brain drain from developing countries has been increasing since the first studies of the phenomenon in the 1960s.7 From 1960-72, only 300,000 highly-skilled workers emigrated from the developing world to the West, while the 1990 U.S. Census revealed that more than 2.5 million highly educated immigrants from developing countries were living in the United States.8 Of course, the effect of high-skilled migration on the developed world's workforce substantial-for example, of the U.S. labor force with doctoral degrees in science and engineering fields, 29% of workers conducting research and development are immigrants.9 As developed economies grow and progress, these countries experience skilled worker shortages and look to immigrants to fill many of the vacancies. According to a Time Magazine article, attracting skilled workers to Canada and keeping them there is perhaps the country's greatest challenge.10 Canada's recent economic growth has led to a severe shortage of workers skilled in information technology, medicine, nursing, teaching, and computer programming-a Canadian Federation of Independent Business survey estimated the deficiency to be between 250,000 and 300,000 workers in small and medium sized businesses alone. A severe nursing shortage being felt across the United Statesfrom Florida to Kentucky to California12-forcing hospitals to recruit from foreign sources. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,939
Score d'incertitude au seuil0,972

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,053
Tête enseignante GPT0,342
Écart entre enseignants0,289 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2004
Routes d'admission1
Résumé présentoui

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