Journal of Geographical Sciences >
The spatial patterns and determinants of internal migration of older adults in China from 1995 to 2015
Liu Ye (1986-), PhD and Professor, specialized in population geography, urban geography and health geography. E-mail: liuye25@mail.sysu.edu.cn |
Received date: 2021-12-24
Accepted date: 2022-08-25
Online published: 2022-12-25
Supported by
National Natural Science Foundation of China(42001153)
National Natural Science Foundation of China(42001161)
Although China was one of the countries with the fastest-growing aging population in the world, limited scholarly attention has been paid to migration among older adults in China. The full picture of their migration in the entire country over time remains unknown. This study examines the spatial patterns of older interprovincial migration flows and their drivers in China over the period 1995 to 2015, using four waves of census data and intercensal population sample survey data. Results from eigenvector spatial filtering negative binomial regressions indicate that older adults tend to migrate away from low cost-of-living rural areas to high cost-of-living urban and rural areas, moving away from areas with extreme temperature differences. The location of their grandchildren is among the most important attractions. Our findings suggest that family-oriented migration is more common than amenity-led migration among retired Chinese older adults, and the cost-of-living is an indicator of economic opportunities for adult children and the quality of senior care services.
LIU Ye , HUANG Cuiying , WU Rongwei , PAN Zehan , GU Hengyu . The spatial patterns and determinants of internal migration of older adults in China from 1995 to 2015[J]. Journal of Geographical Sciences, 2022 , 32(12) : 2541 -2559 . DOI: 10.1007/s11442-022-2060-z
Figure 1 Administrative divisions of provincial-level regions in China |
Table 1 Independent variables for models |
Variable | Description |
---|---|
POPi,j | Population sizes of older adults in the origin and destination provinces, persons a |
DISTij | Minimum travel time by rail between provincial capitals of the origin and destination provinces, hours b |
RCONSi,j | Per capita consumption expenditure of rural residents in the origin and destination provinces, yuan a |
UCONSi,j | Per capita consumption expenditure of urban residents in the origin and destination provinces, yuan a |
TEMPi,j | Average temperatures difference between January and July in the provincial capitals of the origin and destination provinces, ℃a |
BEDi,j | The number of hospital beds per ten thousand population in the origin and destination provinces, beds a |
MIGij | The number of migrants under the age of 16 between the origin and destination provinces, persons c |
Source: a China Statistical Yearbook, 1996-2016. b National Railway Passenger Train Timetable. c 2000 and 2010 population censuses and 2005 and 2015 1% population sample surveys. |
Table 2 Top 5 provinces of in-migration and out-migration of older adults in China, 1995-2015 |
1995-2000 | 2000-2005 | ||||||
---|---|---|---|---|---|---|---|
In | Out | In | Out | ||||
Guangdong | 60200 | Heilongjiang | 49346 | Guangdong | 108899 | Heilongjiang | 90739 |
Beijing | 44889 | Sichuan | 48360 | Tianjin | 75012 | Sichuan | 70986 |
Shandong | 43278 | Anhui | 30192 | Shanghai | 70727 | Anhui | 57507 |
Jiangsu | 42652 | Jiangxi | 28313 | Jiangsu | 66339 | Shandong | 53416 |
Shanghai | 39753 | Hebei | 26564 | Beijing | 64453 | Henan | 47508 |
2005-2010 | 2010-2015 | ||||||
In | Out | In | Out | ||||
Guangdong | 667354 | Sichuan | 441041 | Guangdong | 201564 | Sichuan | 125536 |
Shanghai | 558934 | Heilongjiang | 424201 | Beijing | 157672 | Henan | 93296 |
Zhejiang | 499986 | Anhui | 358934 | Jiangsu | 111183 | Heilongjiang | 89813 |
Beijing | 486301 | Hunan | 333673 | Shanghai | 102896 | Hebei | 80023 |
Jiangsu | 322094 | Henan | 303145 | Zhejiang | 62677 | Anhui | 78891 |
Figure 2 Migration efficiency of older adults in China by province in 1995-2000 (a), 2000-2005 (b), 2005-2010 (c), and 2010-2015 (d) |
Figure 3 The 25 largest interprovincial migration flows of older adults in China in 1995-2000 (a), 2000-2005 (b), 2005-2010 (c), and 2010-2015 (d) |
Table 3 NBRM and ESF NBRM on driving forces for interprovincial migration of older adults in China, 1995-2015 |
Dependent variable | NBRM | ESF NBRM | ||
---|---|---|---|---|
Estimate | z value | Estimate | z value | |
POPi | 0.418*** | 6.140 | 0.494*** | 6.670 |
POPj | 0.014 | 0.220 | 0.072 | 1.060 |
DISTij | -0.665*** | -12.410 | -0.686*** | -11.190 |
RCONSi | -1.009*** | -3.490 | -0.978*** | -3.170 |
RCONSj | -0.031 | -0.120 | 0.486* | 1.740 |
UCONSi | 0.407 | 1.000 | 0.325 | 0.790 |
UCONSj | 2.084*** | 5.750 | 1.935*** | 5.280 |
TEMPi | 0.509** | 2.590 | 0.790*** | 3.670 |
TEMPj | 0.025 | 0.150 | 0.105 | 0.570 |
BEDi | 0.573** | 2.590 | 0.513** | 2.230 |
BEDj | 0.010 | 0.050 | -0.293 | -1.500 |
MIGij | 0.166*** | 13.930 | 0.155*** | 12.790 |
year2 | 0.108 | 0.660 | 0.062 | 0.380 |
year3 | -0.352 | -1.280 | -0.533* | -1.840 |
year4 | -2.236*** | -5.790 | -2.636*** | -6.170 |
Constant | -24.509*** | -7.260 | -29.450*** | -8.080 |
Eigenvectors | — | 10 | ||
MC of residual migration flows | 0.215*** | 23.373 | 0.197*** | 21.530 |
N | 3480 | 3480 | ||
Log likelihood | -8391.829 | -8346.159 | ||
AIC | 16815.660 | 16744.320 | ||
BIC | 16908.480 | 16895.160 |
Notes: ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. The variables of year2, year3, year4 denote the periods of 2000-2005, 2005-2010, and 2010-2015, respectively. NBRM, negative binomial regression model; ESF, eigenvector spatial filtering; MC, Moran’s I coefficient; AIC, Akaike information criterion; BIC, Bayesian information criterion. |
Table 4 ESF NBRM on the evolution of driving forces for interprovincial migration of older adults in China, 1995-2015 |
- Estimate | *year2 Estimate | *year3 Estimate | *year4 Estimate | |
---|---|---|---|---|
RCONSi | -0.796 | -1.029 | -0.112 | -0.001 |
RCONSj | 0.313 | 0.103 | 0.803 | -1.218 |
UCONSi | -1.420* | 3.809*** | 1.744* | 1.513 |
UCONSj | 2.204*** | -0.524 | -0.221 | 3.043*** |
TEMPi | -0.178 | 1.357*** | 0.959* | 1.699*** |
TEMPj | 1.034** | -1.257*** | -1.840*** | -1.563*** |
BEDi | 1.654*** | -1.898*** | -0.548 | -1.305* |
BEDj | -0.031 | 0.456 | 0.904* | -1.183 |
MIGij | 0.139*** | 0.023 | 0.062** | -0.020 |
Constant | -22.361*** | |||
Other control variables | Have been controlled | |||
Eigenvectors | 10 | |||
N | 3480 | |||
Log likelihood | -8261.423 | |||
AIC | 16628.850 | |||
BIC | 16936.320 |
Notes: ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. The variables of year2, year3, year4 denote the periods of 2000-2005, 2005-2010, and 2010-2015, respectively. NBRM, negative binomial regression model; ESF, eigenvector spatial filtering; MC, Moran’s I coefficient; AIC, Akaike information criterion; BIC, Bayesian information criterion. |
[1] |
|
[2] |
|
[3] |
|
[4] |
|
[5] |
|
[6] |
|
[7] |
|
[8] |
|
[9] |
|
[10] |
|
[11] |
|
[12] |
|
[13] |
|
[14] |
|
[15] |
|
[16] |
|
[17] |
|
[18] |
|
[19] |
|
[20] |
|
[21] |
|
[22] |
|
[23] |
|
[24] |
|
[25] |
|
[26] |
|
[27] |
|
[28] |
|
[29] |
|
[30] |
|
[31] |
|
[32] |
|
[33] |
|
[34] |
|
[35] |
|
[36] |
|
[37] |
|
[38] |
|
[39] |
|
[40] |
|
[41] |
|
[42] |
|
[43] |
|
[44] |
|
[45] |
|
[46] |
|
[47] |
|
[48] |
|
[49] |
|
[50] |
|
[51] |
|
[52] |
|
[53] |
|
[54] |
|
[55] |
|
[56] |
United Nations Department of Economic and Social Affairs (UNDESA), 2020. World Population Ageing 2019, United Nations.
|
[57] |
|
[58] |
|
[59] |
|
[60] |
|
[61] |
|
[62] |
|
[63] |
|
/
〈 | 〉 |