NDLI: Exploring the Determinants of Migrant Workers’ Willingness to Buy Houses in Cities: A Case Study in Xi’an, China

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Exploring the Determinants of Migrant Workers’ Willingness to Buy Houses in Cities: A Case Study in Xi’an, China

Content Provider	MDPI
Author	Zhang, Xiaoning Qu, Mei Jin, Zhendong
Abstract	Migrant workers’ buying houses in cities can not only help to reduce the number of unsold houses but also improve the efficiency of the use of rural residential lands. A framework is constructed to study how individual resource endowment and the compensation policy of quitting rural residential land act on migrant workers’ willingness to buy houses in cities. The paper adopts the logistic regression model with the data collected from 410 migrant workers in Xi’an. The results can be drawn as follows: firstly, migrant workers’ desire for buying houses in cities has a close relationship with their individual resource endowment; secondly, there is a gap between the existing compensation policy and migrant workers’ actual preference for the compensation policies. Thirdly, the existing compensation policy cannot fully exert its impact. As a result, when migrant workers are allowed to choose their most preferred policies in light of their own conditions, both the policy and resource effect will become more remarked. Thus, the design of compensation policies for quitting rural residential land should take full account of migrant workers’ individual resource endowments in order to provide them with selective compensation mechanisms. The conclusion provides a policy reference for cities where the house prices are close to that of Xi’an (11,000 yuan/square m).
File Size	460800
File Format	PDF
e-ISSN	20711050
DOI	10.3390/su10010062
Journal	Sustainability
Issue Number	1
Volume Number	10
Language	English
Publisher Date	2017-12-28
Access Restriction	Open
Subject Keyword	individual resource endowment compensation policy rural residential land citizenization migrant workers willingness
Content Type	Text
Resource Type	Article

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