As a vital source of the demographic dividend, migrant workers living in urban villages have positively contributed to urban economic development and the improvement of urbanization. Although urban villages have had a great impact on public health due to the shabby environments and poor public safety, the large-scale demolition of the urban villages, the supply of affordable housing for migrant workers has decreased drastically, which may lead to the outflow of many migrant workers and consequently affects the sustainable operations of cities. Therefore, this paper takes Hangzhou as an example to study the impact of urban village redevelopment on migrant workers and their migration decisions during urban village redevelopment process. The finding indicates that migrant workers are significantly impacted by large-scale demolition. (1) The number of affected migrant workers is huge. For example, 657,000 migrant workers who lived in around 178 urban villages are affected in Hangzhou (34,468 households). (2) The increase in rent is obvious. (3) Strong expulsion effect: nearly 1/3 migrant workers will decide to leave the city because of the demolition. Furthermore, our binary logistic regression model suggests that the commuting time, living satisfactory, and the rent affordability are factors significantly affecting migration workers' decision to leave and stay in the city. The housing quality and comfort indicators are not significant. This indicates that convenience for employment and high rent avoidance are the major characteristics of migrant workers' housing choice. Hence, in addition to considering whether the harsh environment is harmful to the public health of urban and residents, the interest and characteristics of migrant workers should be considered during the current urban village demolition process. While simply demolishing urban villages, government needs to provide a relatively sufficient amount of low-cost and affordable housing for migrant workers in case migrant workers leave the city in large numbers due to lack of suitable housing in the city.
Population urbanization is crucial to establishing a harmonious society. However, the phenomenon of population semi-urbanization is becoming an issue of ever-increasing concern in China. More and more immigrants from rural areas work and live in the city, but their roots remain in the rural area. This paper aims to analyze the influence mechanism of government competition on population semi-urbanization through land supply structure. The study’s theoretical analysis and empirical analysis results are based on the panel data of 105 key prefecture-level cities in China from 2007 to 2017. The results demonstrate that: (1) land finance and land-motivated investment engendered by government competition lead to an imbalance in the land price structure, further increasing the rate of population semi-urbanization; (2) land finance does not lead to population semi-urbanization through the land area structure; and (3) land-motivated investment aggravates the imbalance in the land area structure, further leading to population semi-urbanization. It is found that government competition in terms of achieving performance indicators affects population semi-urbanization by adjusting the land supply structure. Efforts should be made to achieve the coordinated development of urbanization, given that the increasing rate of population semi-urbanization will almost certainly aggravate social instability.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.