Comments by Soohyung Lee, on Quest for Talents: Attraction and Retention of Highly Skilled Overseas Chinese in the United States and Canada
Bibliographic record
Abstract
Soohyung Lee: Acquiring talent is critical for an organization to thrive. Advanced countries have been using prestigious programs to attract foreign talent, notably from less-developed countries (e.g., the Fulbright fellowship program in the United States). Nowadays, such talent acquisition efforts have attracted considerable attention in many countries experiencing rapid population aging and declining fertility (OECD 2023a). In securing talent, China plays a unique role. Not only has it been a significant source of immigrants for developed countries (OECD 2023b), but it has also been making national efforts to aggressively attract talent—both Chinese and non-Chinese (e.g., the Thousand Talents Program).Despite the wide range of economic studies on immigration, there are relatively few studies focusing on recent talent acquisition efforts. In this regard, this paper provides valuable quantitative insights into the key factors influencing the migration of Chinese individuals in China and abroad. This paper utilizes two sets of surveys from 2021 and 2022: one on Chinese individuals currently residing abroad and another on Chinese individuals who migrated back to China from abroad. For both groups, the survey asks respondents about the importance of 15 socioeconomic and cultural factors in considering their relocation decisions.1 For the latter group, the survey additionally inquires about the extent to which the returning Chinese immigrants were satisfied with their income and career in China. Based on OLS, probit, and SNP regression analyses, the paper reports that those who are concerned about spousal employment, trade relations, and the rule of law are less willing to migrate than others. Among those who returned to China, individuals whose decision to migrate was based primarily on talent policy programs and the desire to find a marriage partner are more satisfied with their career and income.I find that this paper addresses an important research question from both practical and academic perspectives and provides valuable information that enhances understanding of the decision-making process of Chinese immigrants abroad or those who have returned to China. That said, I have two suggestions that could strengthen the paper.The first pertains to the sample. Information about the population represented by the survey participants is crucial for readers to fully understand the results. The paper explains that the participants were recruited through networking, but more details on this process are needed. Furthermore, I find some key participant information is missing, including when they migrated abroad and/or returned to China, where they relocated, and whether they are currently married and/or have a child. With this information, we could compare their sample with some nationally representative data, allowing us to gauge the extent to which the findings from this paper may be applicable to other settings.The second suggestion concerns the impact of the returning migrants. The paper documents that some survey participants consider public talent acquisition policies to be an important factor in their migration decisions, suggesting the positive impacts of these policies. If there is variation in the intensity of returning migrants across locations, it would be possible to measure the impacts of having more talent from abroad on economic outcomes and academic performance. By measuring this policy impact, we could gauge the cost-effectiveness of talent acquisition programs in China, which would be useful to other countries pouring their resources into talent acquisition.The views expressed here are those of the author and do not necessarily reflect the views of the Bank of Korea or the Seoul National University.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".