4.7 Article

Macroeconomic determinants of high-tech migration in China: The case of Yangtze River Delta Urban Agglomeration

Journal

CITIES
Volume 107, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.cities.2020.102888

Keywords

High-skilled immigration; Talent policy; Urban migration; Dynamic panel; System generalized method of moments estimator

Categories

Ask authors/readers for more resources

With an increasing demand for high-skilled labors to fuel their high-technology industries, many Chinese cities have implemented a variety of incentive policies to compete for talents. As an attempt to evaluate the efficacy of local talent incentive policies and other factors that operate to attract and secure labors with needed skills and expertise, this study presents the first assessment of regional talent competition within the Yangtze River Delta Urban Agglomeration (YRDUA) on 150,000 sampled high-tech professionals, internalizing the interplay between the development of high-tech industries and economic growth, using a two-equation dynamic panel data model for the period from 2008 to 2018. Results show that while cities that are more ambitious in developing their high-tech industries appeal to a larger size of high-skilled talents, government interventions are more decisive. Also, the role of economic attractiveness in people's destination choices nowadays is not as important as before since people tend to leave for cities with better job opportunities and incentives for easier settlements. Policy incentives with lower-intensity rewards deserve more attention as it appears more effective in high-tech talent attraction. This study fills the research gaps in investigating macroeconomic determinants of high-skilled migration within one of the world's largest urban agglomerations.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available