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Record W7110647008

Causes of the Vietnamese Brain Drain Syndrome

2012· article· W7110647008 on OpenAlexaboutno aff

Bibliographic record

VenueScholarly and Creative Works from DePauw University (DePauw University) · 2012
Typearticle
Language
FieldSocial Sciences
TopicVietnamese History and Culture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEmigrationBrain drainVietnameseDeveloping countryGross domestic productPopulationInvestment (military)Human capital
DOInot available

Abstract

fetched live from OpenAlex

In 2010, Vietnam had more than 2 million emigrants residing in countries such as: the United States, Australia, Canada, Germany, France, the Republic of Korea, Japan, and the United Kingdom (World Bank). This number of emigrants accounted for 2.5% of the whole domestic population. Also according to this date, the emigration rate of tertiary-educated population was 27.1% in 2000. Due to relatively high number of emigrants, Vietnam has experienced rapid growth in receipts of remittances recently. Hernandes-Cross (2005) reports that the amount of remittances sent to Vietnam from other countries reached USD $1.2 billion in 1999. By 2003, this number has grown to $2.6 billion. In terms of Gross Domestic Product (GDP), the increase in remittances represents a growth from 4.4 percent of GDP in 1999 to 7.4 percent of GDP in 2003. Since 2000, foreign remittances to Vietnam have been larger than official development assistance and at a comparable level to foreign direct investment (Donald 2008). Despites benefits from remittances, emigration imposes a substantial cost on Vietnam in terms of lost talent and human resources, or brain drain. The term “brain drain” is normally used to describe the outflow of highly skilled workers from developing to developed counties. In ofher words, i refers to the departure of the mos talented individuals at an appreciable rate in a way that is thought to be detrimental for sending countries (Bushnell and Choy 2001). Although it might be argued that the loss in human capital can be offset by the “amount of remittances sending home, Taylor (1996) showed an overreliance on labor exporting as a strategy for economic development generally produces disappointing results. In countries with relative labor surplus like Vietnam, policies that facilitate emigration to capture a significant share of the remittances, may provide a valuable supplement to, but not a substitute for, a well-designed and carefully implemented national development policy (Taylor 182). It is true that incomes from remittances can be invested in human capital for long-term economic growth, but inefficient policies to retain high skilled workers within Vietnam will counter that effort. This leads to the argument that overall brain drain has negative effects on the development of Vietnam and the government should intervene, possibly restricting emigration to protect the economy's growth. Although, there have been previous studies about causes of low skilled workers emigration (Dang 2008, Pham 2011), there has been hardly any study about the emigration of high skilled workers. Lacking such data, we cannot determine appropriate policies to retain those workers. Therefore, this study aims to provide a model about causes leading to brain drain in Vietnam at micro, meso, and macro-level. Its results suggest possible policies for the Vietnamese government to preserve the stock of high skilled workers within the country. The paper is organized as follows. Section 2 reviews previous literature on cause and consequences of brain drain. Using that information, Section 3 develops hypotheses about the impacts of different factors on the emigration decision and the returning decision. Section 4 provides detailed description of methods used to collect data. Section 5 presents data, Section 6 analyzes results of this study and Section 7 concludes.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.003
Science and technology studies0.0070.004
Scholarly communication0.0000.008
Open science0.0020.001
Research integrity0.0010.002
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 head0.014
GPT teacher head0.212
Teacher spread0.198 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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

Citations0
Published2012
Admission routes1
Has abstractyes

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