Elderly Migration in China: Types,Patterns,and Determinants

Abstract

We examined the migration patterns of older adults in China and the determinants associated with migration. Using select data from the 2011 China Health and Retirement Longitudinal Study (CHARLS), we conducted a spatial analysis to explore the geographical patterns of different types of older migrants. The relationships between personal, environmental attributes, and migration were examined using logistic-linear modeling techniques. Approximately 6.6% of the Chinese adults aged 60 and older migrated in the past 10 years. Elderly migration occurred primarily in metropolitan areas and frontier provinces in China. Personal attributes, family structure, and housing conditions were associated with migration. The spatial patterns were associated with personal culture background, social policy, and regional development. The implications of elderly migration, with respect to establishing proper social policy and paying attention to the living environment of both migrant and non-migrant elders were discussed.

Keywords

China elderly migrants long-distance migrate short-distance migrate migration pattern

Introduction

According to the Sixth National Population Census of the People’s Republic of China, the number of people aged 60 years and older was more than 99 million in 2010, which represented 15% of the total population. As the Chinese “baby boomer” generation ages, this population is expected to increase rapidly in the coming years. Meanwhile, the urbanization process in China is expanding dramatically, with 53% national region areas urbanized in 2012 compared with only 21% in 1982 (National Bureau of Statistics of China, 2013). Because of changing family support systems and diversified elder care choices, an increasing number of people choose to migrate in their later life (Lu & Song, 2006; Omelaniuk, 2005; Tong & Piotrowski, 2012). As older people move, they face many challenges in health care and social welfare systems, which require adjusting policies in both departure and destination communities. The living environment of older people presents both push and pull factors that could have influenced their migration decision. Identifying these factors will help policy makers and government administrators recognize and understand the issues confronting older migrants and in creating aging-friendly initiatives that help older adults successfully age in place, or to achieve their goal of moving.

This study focuses on the migration patterns of older adults in China and the major outcomes of migration. The following questions were addressed

Research Question 1: What are the major migration patterns of older adults in China?

Research Question 2: What are the common characteristics and motivations of elderly migrants?

Research Question 3: What are the main impacts of migration?

We hypothesized that migration is associated with health conditions, family structures, personal income, and living environments, with demographic background such as age and gender also playing important roles. Driven by economic and health factors, older people with higher income may move to cities with mild climate and good housing conditions, while low-income people may choose to move to where their children live or return to their hometown.

Literature Review

Migration Patterns of Older Adults

The study of elderly migration in the Western world began in the 1970s. Post-retirement moves have become increasingly common in recent decades (Serow, 1996) resulting in increased concentrations of the older population in distinct geographic areas. In the United States, older migrants living in the north left a wide range of origin communities for a smaller number of primary resort-like destinations located in the sun-belt areas (Golant, 1990; Graff, & Wiseman, 1990; McCarthy, 1983; Rogers & Frey, 1992). Wiseman (1980) identified three types of migrations: (a) full-time migration to a new community, (b) seasonal migrations between homes in different climates, and (c) relocation to a new type of residence within the same general area.

Relatively little is known about migration patterns of older adults in China. Chai, Tahara, and Li (2006) observed that the migration rate of older adults in China was higher in the cold northern regions and lower in the warm southern areas. Beyond the climate variations, they attributed this pattern to cultural beliefs: the residents of northern regions of China have a strong tradition of returning to the place of their birth after retirement. (Zhang, & Zhou, 2013) explored the migration selectivity of Chinese older adults using the 2005 census data. They found that China’s elderly population tended to move to economically developed regions, such as big cities. Family factors were the main migration motivations of urban older adults. Conversely, Xie and Zhou’s (2013) examination of the spatial location of older people living in major cities in China using 2010 census data suggested that in metropolitan areas, the elderly population spread outward from the city center to the suburbs, and gradually scattered in the countryside.

Influencing Migrate Factors

Litwak and Longino (1987) proposed three typical conditions underlying relocation in late life. Amenity or lifestyle-driven moves referred to older adults who made moves to pursue their ideal retirement lifestyle. Assistance moves were related to moving to close kin due to decreasing competence. Nursing home moves occurred to secure specific care for older adults who needed attention beyond family support. Migration behavior also changes with age. Speare (1974) found that residential satisfaction was directly contingent upon background circumstances such as individual needs, housing and neighborhood factors, and social bonds that increased with age. Older people with higher levels of residential satisfaction tended to stay in their current living arrangements rather than moving (Speare, 1974). Previous researchers concluded that the major reasons for elderly migration included housing dissatisfaction, declining health conditions, improving residential amenities, reducing the cost of living, and closeness to relatives (Lawton, 1980; Walters, 2002; Wiseman, & Roseman, 1979).

Older adults in China migrated both voluntarily and involuntarily. Some older adults chose to migrate due to their increasing needs for medical care associated with aging, whereas some were forced to move because their residential areas were transformed as part of urban renewal projects (Liang, Chen, & Gu, 2002; Liu, 2014; Tian, 2012). Family factors and regional economic and social welfare differences also influenced the migration pattern. Older people tended to move to places where a full covered medical care system and social welfare system were approachable (Chai, et al., 2006). Family factors, such as looking after grandchildren, were the main migration motivations of urban older adults, whereas regional income discrepancy was the most significant factor for their rural counterparts (Zhang, & Zhou, 2013).

Public policy affected elderly migration in China in many ways. The elimination of migration restrictions in late 1970s led to a surge of internal migration from rural to urban areas in China, but that type of migration tended to be temporary and circular, rather than permanent. Experienced farmers went to work in cites during the slack seasons, and went back to farm during busy seasons (Lu, & Song, 2006; Tong, & Piotrowski, 2012). Although the increased manufacture industries added many working positions in the central rural areas, it did not prevent interprovincial migration (He, & Ye, 2014). Furthermore, the unique “One Child Policy” in China has been implemented over 30 years. Compared with prior generations, today’s Chinese older people tend to seek emotional support by following their adult children (Wu, 2013). The permanent residency registration system, which applied in main cities, and the urban–rural disparity in social welfare provision also led elders living in rural areas to move to urban cities (Hu, et al., 2011).

Challenges of Migration

China is a large and ethnically diverse country; cultural and societal norms influence the choice to migrate. Despite trying to use migration as a tool to age well, many migrants face significant barriers such as segregation, discrimination, and lack of medical care. For example, rural migrants are largely excluded from urban services, such as access to health care (Altschuler, 2013; Hu, et.al., 2008; Tong, & Piotrowski, 2012). Lu and Song (2006) investigated the determinants that affected wages of migrant workers based on survey data from Tianjin. The results suggested that there was a large income gap between rural migrants and urban residents, which could lead to a shortage of pensions and other provisions in their later life.

The geographic separation of young people and their parents also led to degraded welfare conditions for elderly individuals (He, & Ye, 2014; Liu, 2014). Moreover, the wave of older migrants toward cities will lead to challenges in building elder care facilities, establishing trans-regional welfare systems, and improving social services (Wu, 2013) to meet their needs.

Method

Sample

The data analyzed for this study were drawn from the 2011 China Health and Retirement Longitudinal Study (CHARLS). The CHARLS collected a nationally representative sample of Chinese people aged 45 and older using a multi-stage stratified, clustered sampling method. The baseline national CHARLS sample included approximately 10,000 households and 17,708 individuals in 150 counties and districts within 28 provinces, municipalities, and autonomous regions in mainland China (excluding Hong Kong, Macau, and Taiwan), and covered 450 village committees. As the population in Hainan, Ningxia, and Tibet were too small for sampling, the data from these provinces were not included. A software package (CHARLS-GIS) was created to make village sampling frames (Feng, et.,al, 2014; Tian, 2012). A structured survey questionnaire with 11 main sections was used to collect national baseline data in 2011, including demographic background, family, health status and functioning, economic status, and housing conditions.

The targeted population in this study was older adults, 60 years and above, who had made at least one residency change in the past 10 years. There were 7,669 out of 17,708 participants who were 60 years and older and 507 were identified as migrants. Based on their departure and current residential locations, individuals who migrated inside the same province were defined as “short-distance migrants,” and those who migrated inter-provincially were defined as “long-distance migrants.” A total of 507 older migrants were identified, which included 466 short-distance migrants, 48 long-distance migrants, and 7 who were both short- and long-distance migrants.

Statistical Analysis

Logistic regression was conducted to examine the effect factors in two types of elderly migration. Model 1 and Model 3 were introduced to examine the correlations between personal demographic attributes and migration behaviors, while Model 2 and Model 4 were used to examine the personal socio-economic attributes. All analyses were run in Stata 12.

Dependent variables

Two dichotomous variables were created to indicate whether the person had made a long-distance move or a short-distance move in the last 10 years (0 = no, 1 = yes). The data from seven people who had made both types of moves were included and analyzed in each regression, because the data contained important information on both types of moves.

Independent variables

Motivations for migration are recognized as a combination of economic and demographic, social, cultural, and politically related push and pull factors (Chow, & Phillips, 1994; He, & Gober, 2003; Tian, 2012). It was assumed that these factors would also have a significant influence on migration outcomes because people generally migrate to improve their lives. The following variables were available from the CHARLS survey and were selected to examine motivational influences on migration.

Demographic characters

Elderly migrations are affected by individuals’ cumulative attributes, including demographic characteristics. (Longino, Bradley, Stoller, & Haas, 2008; McHugh & Mings, 1996). In this study, background information about the participants included age, gender, marital status, urban/rural residency, and education level.

Personal income

Income is an important factor that affects the ability to live independently and make necessary life changes (Hu, et al., 2008; Litwak, & Longino, 1987). In this study, total personal income included pensions and individual-based transfer income such as social subsidies. Although rural residents may have income from agriculture work, it was not included in the CHARLS.

Physical health

Researchers have noted a relationship between elderly migration and physical health. For example, the probability of moving decreased when disability level increased (Longino, Jackson, Zimmerman, & Bradsher, 1991; Speare, Avery, & Lawton, 1991). For the current study, measures of physical health were Activities of Daily Living (ADLs), Instrumental Activities of Daily Living (IADLs), and self-evaluated health. Six types of ADLs were assessed in the 2011 CHARLS: difficulty in (a) dressing, (b) bathing or showering, (c) eating, (d) getting into or out of bed, (e) using the toilet, and (f) controlling urination/defecation. Five types of IADLs were also included: difficulty in (a) doing household chores, (b) preparing meals, (c) shopping for groceries, (d) managing money, and (e) taking medications. Respondents reported on difficulties with ADLs and IADLs using scaled values ranging from 1 (no difficulty) to 5 (absolutely cannot do it). Overall, difficulty was represented by the sum of all values. Scores on the total difficulty scale ranged from 11 to 44, which was divided into four groups: no difficulty (11-13), minor difficulty (14-18), some difficulty (19-25), and severe difficulty (26 and above). The variable “self-evaluated health” was also chosen as an index of physical health, which used a 5-point scale from 1 (very good) to 5 (definitely poor).

Mental health

Mental health is critical for older adults to maintain independence (Speare et al., 1991). Symptoms of depression were assessed using 10 questions from the Center for Epidemiological Studies Depression Scale (CES-D) asking about the participants’ feelings during the week before their interview. Responses to the items ranged from 0 to 3 and were summed for a total depressive symptoms score. The participants’ total scores ranged from 0 to 30, with higher values indicative of more depressive symptoms. In addition, questions about life satisfaction were asked and measured using a score from 1 (very good) to 5 (definitely poor).

Housing quality

Housing quality affects the living environment and residential satisfaction of older adults, which may have indirect influences on moving (Lawton, 1980). Two variables were used to define housing quality: (a) house size and (b) house basic infrastructure, which included electricity, water, gas supply, heating, telephone, and Internet. The house infrastructure was coded as a categorical variable with 0 standing for no facilities available and 7 for all the facilities included. The score was divided into three categories: good (6-7), fair (4-5), and poor conditions (0-3).

Family living arrangement

Family support is a critical factor in the Chinese traditional elder care system. The location of family members, especially adult children, often influences moving decisions of older adults (Wiseman, 1980). The number of adult household members and whether the adult children were living with the older parents were included in this analysis.

Spatial Analysis

To explore the migration patterns in a geographic perspective, the “Migrant Mobility Ratio” (MMR) was used to measure the short-distance migration behavior in each province. MMR is computed by dividing the number of short-distance migrates by the sum of all the 60+ samples of that province/municipality/autonomous region, multiplied by 100:

{MMR}_{i} = \frac{S D_{i}}{{Total Sample}_{i}} \times 100 % .

Because the sample size of long-distance migrants was relatively small, the MMR could not be applied. The locations of migrants’ departure area and current living places were mapped to see which provinces were more attractive or unattractive. All the spatial data were analyzed using the geographic information system (GIS).

Results

Demographic Characteristics of Migrants

Between 2001 and 2011, approximately 6.6% of CHARLS participants aged 60 and older had migrated either short or long distance. About 6.1% had made short-distance moves and 0.5% had made long-distance moves (Table 1). Only about one fourth of the total sample of older adults (23.9%) lived in urban areas in the year 2011, compared with 86.8% among older migrants. There were no obvious gender differences between the total samples and short-term migrants; however, for long-distance migration, females had a slightly higher proportion (54.2%).

Table 1.

Demographic and Socioeconomic Characteristics of Participants Aged 60+.

	Total sample		All migrants		Short-distance migrants		Long-distance migrants
	n	%	n	%	n	%	n	%
Region
Urban	1,832	23.89	440	86.79	392	84.12	39	81.25
Rural	5,837	76.11	67	13.21	74	15.88	9	18.75
Gender
Male	3,844	50.16	249	49.21	229	49.14	22	45.83
Female	3,820	49.84	257	50.79	237	50.86	26	54.17
Age
60-69	4,742	61.83	311	61.34	281	60.30	36	75.00
70-79	2,264	29.52	159	31.36	150	32.19	10	20.83
≥80	663	8.65	37	7.30	35	7.51	2	4.17
Marital status
Married	6,018	78.47	397	78.46	357	76.61	41	85.42
Divorced	48	0.63	7	1.38	10	2.15	0	0.00
Widowed	1,526	19.90	101	19.96	97	20.82	7	14.58
Single	72	0.94	1	0.20	1	0.21	0	0.00
Education
Illiterate	2,832	36.93	117	23.21	108	23.18	10	20.83
Primary	3,353	43.72	181	35.92	166	35.62	17	35.41
Middle	1,080	14.08	121	24.01	109	23.39	15	31.25
Vocational	226	2.95	50	9.92	46	9.87	4	8.33
College and above	161	2.10	35	6.94	34	7.27	2	4.17
Total	7,669	100.00	507	6.61	466	6.08	48	0.53

Compared with the total sample of older adults, individuals aged 60 to 69 were more likely to make long-distance moves (75.0%). Marital status showed no significant difference between total sample and short-distance migrants. However, the percentage of married older adults (85.4%) was higher among long-distance migrants. The percentage of widowed older adults was lower in this group (14.6%).

Both short- and long-distance migrants had a relatively higher degree of education. Compared with the total sample, there were more people who completed middle school (24.0% vs.14.1%), vocational school (9.9% vs. 2.9%), and college or above (6.9% vs. 2.1%).

Spatial Distribution of Older Migrants

For short-distance migration, the migration mobility rate is higher in the northern frontiers, central regions, and major metropolitan areas, such as Yangzi River Delta (Figure 1). The highest short-distance MMR appeared in Heilongjiang (30.4%), Shanghai (30.0%), and Xinjiang (21.3%). In addition, Inner Mongolia (17.2%), Beijing (10.6%), Jilin (10.5%), and Hubei (10.5%) also showed high mobility rates for older adults. The next level of migration was in the central regions in China: Hunan (9.2%), Shanxi (8.9%), and Jiangxi (7.7%). In contrast, provinces in southwest showed low MMRs (Table 2).

Figure 1.

Spatial distribution of short-distance older migrants.

Table 2.

Migrate Mobility of Old Adults by Province.

Rank	Province name	Long distance	Short distance	All older adults	Migrate mobility (%)
1	Heilongjiang	2	38	125	30.40
2	Shanghai	3	9	30	30.00
3	Xinjiang	0	10	47	21.28
4	Nei Mongol	3	50	290	17.24
5	Beijing	5	7	66	10.61
6	Jilin	1	18	171	10.53
7	Hubei	0	24	228	10.53
8	Hunan	0	34	371	9.16
9	Shaanxi	5	22	247	8.91
10	Jiangxi	2	31	403	7.69
11	Liaoning	2	17	227	7.49
12	Henan	3	39	566	6.89
13	Shanxi	0	15	223	6.73
14	Zhejiang	3	20	317	6.31
15	Jiangsu	2	20	375	5.33
16	Hebei	1	13	298	4.36
17	Yunnan	2	19	453	4.19
18	Sichuan	2	27	763	3.54
19	Guangdong	6	13	387	3.36
20	Gansu	1	6	187	3.21
21	Guangxi	0	10	320	3.13
22	Shandong	0	13	611	2.13
23	Qinghai	0	1	59	1.69
24	Chongqing	0	2	124	1.61
25	Anhui	0	6	434	1.38
26	Guizhou	0	1	101	0.99
27	Fujian	0	1	190	0.53
28	Tianjin	5	0	56	0.00
Total		48	466	7,669

Most long-distance migrants came from inland provinces, especially Xinjiang, Qinghai, and Sichuan, followed by the central and northeast region (Figure 2, & Figure 3). Beijing, Tianjin, Shaanxi, and Guangdong were the most frequent destinations for older migrants. In addition, Guangdong was the province with the highest number of outside and inside migrations. Many Guangdong residents migrated to other places later in life, whereas there were a large number of older adults who migrated into Guangdong during the past 10 years.

Figure 2.

Spatial distribution of long-distance move-out older migrants.

Figure 3.

Spatial distribution of long-distance move-in older migrants.

Migration Impacts

Table 3 shows the correlations between migration and life status. In Model 1 and Model 3, living region was proven a strong effect factor to elderly migration. For short-distance migrants, younger age was a positive factor, while a lack of education was a negative attribute that contained migration.

Table 3.

Logistic Regression Coefficients.

Independent variables	Short-distance migration		Long-distance migration
Independent variables	Model 1	Model 2	Model 3	Model 4
Demographic attributes
Male	−1.130		0.310
Female^a	—		—
Region
Urban	3.052***		2.643***
Rural^a	—		—
Age
60-69	0.348**		0.910
70-79	0.294		0.309
80+^a	—		—
Marital status
Married	0.548		8.582
Divorced	1.116		—
Widowed	0.730		8.175
Single^a	—		—
Education
Illiterate	−0.542*		0.785
Primary	−0.726*		0.658
Middle	−0.730		−0.917
Vocational^a	—		—
College and above	0.291		−0.894
Personal income
Group 1 (0-25%)^a		—		—
Group 2 (25%-50%)		−0.296		−0.662
Group 3 (50%-75%)		−0.407***		−0.962***
Group 4 (75%-100%)		−0.107		−0.748***
Family structure
Household children^b		0.169		0.298
House member^b		0.208***		−0.245
Mental health
Life satisfaction^b		−0.214***		−0.099
Depression score^b		0.008		0.005
Physical health
Self-evaluate health		−0.067		−0.204
Difficulties in daily life
No difficulties		−0.270		−0.103
Minor difficulties		−0.356		−0.425
Some difficulties		0.014		−0.286
Serious difficulties^a		—		—
Housing condition
House area^b		−0.001		−0.012***
Housing quality
Poor		−0.476***		−0.811***
Fair^a		—		—
Good		0.192		−0.162
Pseudo R²	.2365	.2386	.1340	.1776
Number of cases	7,669	6,428	7,497	6,428

Designates reference category.

Denotes continuous variable.

p < .1. **p < .05. ***p < .01.

In Model 2 and Model 4, as expected, housing quality was significantly related with both short- and long-distance migrations, which indicated people ended up with better housing. Housing size showed a negative effect on long-distance migration, but had no correlation with short-distance migration. Life satisfaction was only positively related to short-distance migration. The degree of depression was not associated with either type of migration, nor were the ADLs/IADLs and self-evaluated health. The assumption was that family structure would have a strong impact on migration among older adults who move to live with their adult children. The results indicated, however, that it was a weak relationship and not statistically significant. When other factors were controlled, the number of household members (including parents, siblings, grandchildren, and caregivers) was strongly related to short-distance migration. Higher personal income was negatively related with both types of migrants, which indicated income might decrease after migration.

Discussion

Several determinants contributed to elderly migration, including personal and housing attributes, inner-family demands, and regional development enforcements. The mutual effects and actions of those factors have shaped a unique migration pattern in China.

Determinant 1: Personal and Housing Attributes

Compared with those who did not move, migrants tended to be younger, more educated, and living in urban areas. Because of the barrier of distance and the cost of moving, along with potential losses that may occur (e.g., pensions), the oldest (80 years and over) groups may find it more difficult to migrate because their physical capacity for adapting to change might decrease with age. This phenomenon was more obvious in long-distance migrants, most of whom were couples in their sixties who moved soon after retirement.

Education level was strongly correlated with urban residency among the older generations in this study. China’s urban–rural dualism has created two different social welfare systems, which operates separately and inconvertibly (Hu, et al., 2011; Lu, & Song, 2006; Tong, & Piotrowski, 2012; Vendryes, 2011). Urban citizens are allowed to keep their pensions and subsidies during citizenship transferring. But for their rural counterparts, with a location-fixed welfare policy, migrants lose their subsidy once they move out of their village. This factor alone is a deterrent for rural older adults’ migration. In addition, rural people, especially those with lower education, are more influenced by the traditional cultural belief that one should die in the place where they were born; thus, they are likely to remain where they are born. The effect of cultural and traditional beliefs has less impact on urban or well-educated residents.

Higher income level was significantly associated with elderly migration, which suggests that financial conditions play an important role in the migration process. Income is fixed and predictable for most retired elders; therefore, only those who consider themselves able to afford the direct and indirect costs of migrating actually move. This phenomenon was more strongly supported by the correlation between long-distance migration and highest income group. Although people may move to live with relatives due to financial issues, they do not have to fall into low-income groups.

Housing condition could be an explanation for migration among older adults. As the data indicated, the older migrants reported better housing condition than those who did not move. This phenomenon may associate with the declined health status of the older group. As people age, their tolerance for the lack of basic amenities may decrease as their capacity for heavy house chores will decrease along with declined health. Climbing many stairs could be another obstacle for older people who live in high-rise apartments without elevators. Similarly, the decreasing mobility will stimulate the need for communication devices such as phone, TV, and Internet. Old people could improve their housing condition by renovation instead of moving. There is a positive relationship between home modification and aging-in-place (Hwang, et al., 2011; Alley, et al., 2007). However, if home modification is impossible due to financial or constructional reasons, moving becomes the only way to secure a supportive living environment. People usually move to a newer house or apartment with more complete amenities.

Housing size, which is also an important element in evaluating living quality, shows no positive correlation on migration. In other words, obtaining a larger house may not be a dominant reason for older adults to migrate. Prior research has suggested that older people in the Western world tend to move into smaller houses to reduce property costs and upkeep (Alley, et al., 2007). Although property tax is not generally collected in China, necessary maintenance and management costs could also be restraint factors for older people moving to larger houses.

Determinant 2: Inner-Family Demands

The traditional structure of intergeneration care that adult children should take care of their aging parents, is changing. According to the results, there was no evidence suggesting that living with children would affect elderly migration. Older adults might migrate to seek more household support or spiritual communities, which do not necessarily come from their children. Prior research has suggested that many older people moved to provide caregiving for their grandchildren (Liu, 2014; Tian, 2012; Zhang, & Zhou, 2013). This is a prevalent phenomenon in the modern Chinese society, especially in urban areas where people endure higher work pressure and are unable to provide enough care for their children. The positive correlation between short-distance migration and household members also suggested that older adults tended to live in a multi-generational family. The inner-family demands are becoming a double-direction compensation, which could motivate elders to migrate.

The data also indicated that one half (50.6%) of older migrants were living with a spouse only or living alone. Instead of living with other family members, they chose a relatively independent life after migration. This phenomenon could be a reflection of contemporary social reality that there is a big group of “empty nest” older adults: Either they prefer this lifestyle or they do not have children around (Wu, 2013). However, migration could benefit older adults in many ways. A positive correlation between higher life satisfaction and short-distance migration provides strong support for this point.

Determinant 3: Regional Development Enforcements

The spatial pattern of the Chinese elderly migrants exhibited regional distinctions, which reflected different developing stages and their effects on migration, both direct and indirect. Most elders made short-distance migrations. Older adults who moved across provinces left frontier or underdeveloped regions for metropolitan areas with higher levels of development, either following their adult children or searching for better social services. Ethnicity, belief, and language could also be important elements that affect their choice of destinations. According to some Western studies, people who belong to the same or similar ethnic group may feel more comfortable moving into adjacent regions/provinces to keep up their habitual lifestyle (Cuba, & Longino, 1991). In China, Shaanxi was the most popular destination for older migrants who came from the northwest region, as it was a relatively developed province containing lots of culture diversities.

For short-distance migrants, the highest mobility rate occurred in metropolitan areas such as Shanghai, which suggested that people living in those areas were more willing to make a residential change in their later life. This phenomenon could be explained in two ways. First, big cities usually have more significant regional discrepancy. People may have been restricted in places of work, but after retirement, they were free to move to other districts that had better social services, medical amenities, and/or living conditions. Second, urban residents have a much higher average income than their rural counterparts, which suggests they could afford to move if they desired.

Industrialization and urbanization processes in China play an important role in contributing migration patterns. Due to resource development in frontier regions, especially in northern China, many people left their farms or ranches to work in mining and its downstream industries. This trend became less prevalent with the decrease in both natural resource reserves and the profit of mining. Some retired miners came home, and some older people moved to mining districts to live with their families. For the central regions, the urbanization and industrial transfer processes affected much of migration patterns. With the implementation of the “Rise of Central China” policy, many individuals in these regions have experienced substantial social transition. Changing of residency, industrial expansion, along with young people leaving villages to work in towns, have affected older persons’ lifestyles. Older people in these communities are forced to migrate as their farmland is occupied or converted for industrial purposes. However, once leaving their farmland they lose all their capital for surviving and earning a living. This has become a critical issue for these migrants, and a difficult task for public policy makers to provide support.

Conclusion

This study explored the migration patterns among older people in China and investigated factors that influence short-distance and long-distance migrations. Results suggested that a considerable number of older adults migrated after retirement for several reasons, including family support, social services, and living condition. The migration pattern also reflected social changes that related to urbanization, economic transformation, and cultural differences in recent years.

Although the study findings shed new light on migration patterns among older adults in China, it is not without its limitations. First, due to the small sample size, we were not able to explore more geographic patterns for long-distance migrations. Migration patterns inside provinces were not available, because system sampling did not include every city. Second, this study admitted some potential aspects that were not included in the CHARLS survey. For instance, the survey did not report whether migrations were voluntary or involuntary, and moving intentions were not included either. Third, a longitudinal study, which can explore the predictors of migration behaviors, was not yet possible because the database only released one nationwide wave. Further study should focus on factors that affect migration decisions, and the micro-migration pattern between city and suburb, which could better address the relationship between elder migration and urban development. Chinese elderly migration motivation showed some similarity with the Western society, such as migrants’ personal attributes and the need for family support. However, the migration behavior does not exactly follow the “typical steps” revealed by prior social scientists. Unlike their American counterparts, Chinese migrants’ destination preferences are not concerned with climate. The “snowbird” pattern is regarded as “seasonal tourism” rather than a separate type of migration. Compared with elders in the West, Chinese elders have less mobility and traditionally rely on family members more as they age. Patterns show that even if Chinese elders do migrate, they usually chose to move where their children are. That is, for Chinese elders, the first and the second reasons for migrating are probably combined—they move to a more supported home within a reasonable distance to their children. This implication can also partly explain the spatial pattern that elderly migration mainly happened in the rapid industrialized and urbanized region with high labor mobility.

Elderly migration, as a strong influencing factor to the development of both departure and destination communities, demands a flexible public policy related to elderly care systems. For departure communities, moving-out older migrants will lead to decreasing local population, which may weaken the local economy and lower the area reputation. For destination communities, moving-in older populations will exacerbate the local financial burden and demand corresponding public services. This dilemma is usually expressed as an urban–rural discrimination in China, which adds inconvenience in migrants’ later lives. An aging society needs essential strategies in building appropriate amenities for the increasing older population. The number of older migrants is growing, despite that the majority of older adults prefer to age in place. Responsive policies and social services are necessary not only to eliminate urban–rural barriers but also to assist older migrants to adapt to new environments.

Footnotes

Acknowledgements

We are grateful for the comments and contribution of Dr. Jon Pynoos, Davis School of Gerontology, University of Southern California; Dr. Karen A. Roberto, Center for Gerontology Virginia Tech; Dr. Rosemary Blieszner, Department of Human Development, Virginia Tech; and Dr. Richard Wood, Utah State University.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Xiaolu Dou, is a PhD candidate in human geography, School of Urban and Environment, Peking University (PKU), Beijing, China, also a Chinese Scholarship Council (CSC) sponsored visiting scholar in Davis School of Gerontology, University of Southern California. She is a research assistant in Center of Real Estate Research and Appraisal in PKU. Her research interests include urban and regional development, elderly housing, migration, and urban policy.

Yujun Liu is a PhD student in Human Development with a simultaneous degree in MPH in Virginia Tech. She holds a Master of Social Work from Washington University in St. Louis. She has worked as a research assistant in Center for Gerontology and Adult Day Care Center in VT. Yujun’s research interests include community senior services, gerontechnology, and dementia caregiving.

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