Analyzing the spatial mechanism of interprovincial migration in China under uncertainty

被引：0
|
作者：
Pu Y. [1 ,2 ,3 ]
Wu Z. [1 ]
Ge Y. [4 ]
Kong F. [1 ]
机构：
[1] School of Geography and Ocean Science, Nanjing University, Nanjing
[2] Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing
[3] Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing
[4] School of Earth Science and Engineering, Hohai University, Nanjing
来源：
Dili Xuebao/Acta Geographica Sinica | 2021年 / 76卷 / 12期
基金：
中国国家自然科学基金;
关键词：
Bayesian model averaging (BMA); China; Network effects; Population migration; Posterior model probability; Posterior probability of variable inclusion; Spatial OD models;
D O I：
10.11821/dlxb202112007
中图分类号：
学科分类号：
摘要：
Population migration process has an innate uncertainty with the increasing complexity of regional socioeconomic development. Bayesian model averaging (BMA) provides a feasible solution to the uncertainty of linear regression models. However, model uncertainties are seldom considered in spatial modeling. To reduce the uncertainties in migration modeling, this paper incorporates BMA approaches with spatial origin-destination (OD) models to quantify the spillover mechanism of interprovincial migration in China, 2010-2015. Specifically, we specified network dependence for migration flows and selected origin/destination's population size, gross domestic product (GDP), real wage, urbanization rate, the number of beds in health facilities per 1000 persons, the number of people over college level per 100000 persons in 2010 and migration stocks between 2005 and 2010 as well as railway travel time between provincial capitals in 2010 as explanatory variables. Among 2615 unique models based on 300000 samples using Markov chain Monte Carlo model combination (MC3), 58 models with posterior probability greater than 0.1% were chosen to estimate explanatory variables' origin effects, destination effects, and network effects. Some findings are as follows: (1) BMA model estimates are more robust and reliable. Compared with results from the single spatial origin-destination (OD) model, the widths of 90% credible interval of different explanatory variables are markedly reduced, indicating the degree of model uncertainty has been greatly decreased. (2) Regional population size, quality, and migration stocks have a significant influence on interprovincial migration processes. After model sampling, the inclusion probabilities of population size and GDP at origins, education level and migration stocks at destinations as well as distance variable are beyond 90%. (3) Network effects of most variables are statistically significant, accounting for more than 40% of their corresponding total effects. Moreover, the spillover effects of real wage, education level, and migration stocks are even greater than their corresponding origin and destination effects. (4) The impacts of most explanatory variables on interprovincial migration would be overestimated without considering uncertainties in modeling migration processes. © 2021, Science Press. All right reserved.
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页码：2964 / 2977
页数：13
相关论文
共 64 条
  • [11] Williams A M, Balaz V., Migration, risk and uncertainty: Theoretical perspectives, Population, Space and Place, 18, pp. 167-180, (2012)
  • [12] Bijak J, Bryant J., Bayesian demography 250 years after Bayes, Population Studies, 70, 1, pp. 1-19, (2016)
  • [13] LeSage J P, Pace R K., Spatial econometric modeling of origin-destination flows, Journal of Regional Science, 48, 5, pp. 941-967, (2008)
  • [14] Fischer M M, Wang J., Spatial Data Analysis: Models, Methods and Techniques, (2011)
  • [15] LeSage J P, Thomas-Agnan C., Interpreting spatial econometric origin-destination flow models, Journal of Regional Science, 55, 2, pp. 188-208, (2015)
  • [16] Lian Lei, An empirical study on the spatial effects in the migration, Population and Economics, 2, pp. 30-39, (2016)
  • [17] Pu Yingxia, Han Hongling, Ge Ying, Et al., Multilateral mechanism analysis of interprovincial migration flows in China, Acta Geographica Sinica, 71, 2, pp. 205-216, (2016)
  • [18] Zhang Hongli, Liang Yinhe, Yang Weiqiong, Market potential, expected wages and interprovincial migration: Based on spatial econometrics model, Journal of Applied Statistics and Management, 35, 5, pp. 868-880, (2016)
  • [19] Gu Hengyu, Shen Tiyan, Liu Zixiang, Et al., Driving mechanism of interprovincial population migration flows in China based on spatial filtering, Acta Geographica Sinica, 74, 2, pp. 222-237, (2019)
  • [20] Shi Bo, Zhang Xinyue, Technology accumulation, spatial spillover and population migration, China Population, Resources and Environment, 29, 2, pp. 156-165, (2019)
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