Identity recognition of rural migrant workers and urban residency willingness:empirical evidence based on Principal Component Analysis and Random Forest Algorithm
Turning the rural migrant workers into citizens is pivotal in supporting the new urbanization and urban-rural integration,thereby fostering common prosperity,while their willingness to stay is the core for them to become citizens.It holds significant importance for the systematic formulation of population and urbanization policies in China to delineate the influencing factors of rural migrant workers'willingness to stay in cities,particularly to identify the core factors.Based on the 2017 China Migrants Dynamics Survey,Principal Component Analysis is used to construct an identity index spanning four dimensions.Subsequently,the Random Forest Algorithm is employed within a unified analytical framework of subject,object,and subject-object interaction,and the influencing factors are assessed of the rural migrant workers'willingness to stay and the subregional heterogeneity is analyzed.The baseline analysis reveals that the interaction-induced sense of indentity recognition between migrant worker subjects and the objects in the inflow areas is the core determinant of their willingness to stay,explaining 36.7％of the variance.Subjective characteristics such as ethnicity and marital status emerge as significant factors influencing rural migrant workers'willingness to stay,accounting for explanatory powers of 24.2％and 20.3％respectively.Moreover,object characteristics,namely the features of the inflow city,exert a moderate influence on rural migrant workers'willingness to stay,explaining 18.8％of the variance.Heterogeneity analysis uncovers that regional disparities in rural migrant workers'willingness to stay primarily stem from object characteristics,particularly the attributes of the inflow city,with greater similarities observed in the eastern and central regions,as well as in the western and northeastern regions.Based on these findings,targeted policy recommendations are provided in terms of focusing on the subject characteristics of migrant workers,optimizing the object characteristics of the inflow place,and then improving the subject-object interaction experience in order to enhance the sense of identity recognition.
common prosperityrural migrant workersidentity recognitionresidency willingnessPrincipal Component AnalysisRandom Forest Algorithm
国家科技期刊平台
NSTL
万方数据
