Bibliographic record
Abstract
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. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".