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Record W233861818

Should the Brain Drain Be Plugged? A Behavioral Economics Approach

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

Bibliographic record

VenueTexas international law journal · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEmigrationImmigrationWageProtectionismBrain drainDeveloping countryPoliticsEconomicsPolitical scienceDevelopment economicsLabour economicsDemographic economicsPolitical economyEconomic growthInternational economicsLaw
DOInot available

Abstract

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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. …

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score0.972

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.342
Teacher spread0.289 · 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 designTheoretical or conceptual
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

Citations2
Published2004
Admission routes1
Has abstractyes

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