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The Journals of Gerontology Series B: Psychological Sciences and Social Sciences logoLink to The Journals of Gerontology Series B: Psychological Sciences and Social Sciences
. 2020 Apr 23;76(Suppl 1):S17–S26. doi: 10.1093/geronb/gbaa042

Successful Aging in East Asia: Comparison Among China, Korea, and Japan

Takeshi Nakagawa 1,✉, Jinmyoung Cho 2, Dannii Y Yeung 3
Editor: Jennifer Ailshire
PMCID: PMC8495754  PMID: 32324214

Abstract

Objectives

Heterogeneity in successful aging has been found across countries. Yet, comparable evidence is sparse except in North America and Europe. Extending prior research, this study examined the prevalence and correlates of successful aging in East Asia: China, Korea, and Japan.

Method

We used harmonized data sets from national surveys. A total of 6,479 participants (aged between 65 and 75) were analyzed. Using Rowe and Kahn’s (1987, 1997) model, successful aging was defined as having no major diseases, no difficulty performing activities of daily living, obtaining a median or higher score on tests of cognitive function, and being actively engaged.

Results

The average prevalence of successful agers was 17.6%. There were variations in the global and specific measures of successful aging within and across countries, even after controlling for individual sociodemographic factors (age, gender, and education). The odds of aging successfully were highest in Japan and lowest in China, especially in the rural areas. Being younger and males were associated with a higher likelihood of successful agers in both global and specific measures.

Discussion

This study observed heterogeneity in successful aging in East Asia. To identify policy implications, future research should explore potential societal factors influencing individuals’ opportunities for successful aging.

Keywords: Cross-national comparisons, Healthy aging, CHARLS, KLoSA, JSTAR

Successful aging has been one of the central research interests in the literature on gerontology over the past half century (Havighurst, 1961; Havighurst & Albrecht, 1953). Since models of successful aging were proposed (Baltes & Baltes, 1990; Rowe & Kahn, 1987, 1997), empirical evidence has been accumulated. Rowe and Kahn (1987, 1997) defined and operationalized successful aging using three objective criteria, namely, avoidance of disease and disability, maintenance of cognitive and physical function, and social engagement. However, the operationalization of successful aging varied across studies (Depp & Jeste, 2006). Even though no complete consensus on how to define and measure successful aging has been reached, most of the past studies defined successful aging, in part, as the absence of physical disability, and, to a lesser extent, the absence of cognitive impairment, with fewer studies including social engagement (Depp & Jeste, 2006). This indicates that the majority of previous studies have defined successful aging more or less in accordance with Rowe and Kahn’s model.

More recently, using the definition of Rowe and Kahn (1987, 1997), large population-based studies have reported the prevalence and correlates of successful aging among older adults aged 65 years and older in North America and Europe (Hank, 2011; McLaughlin, Connell, Heeringa, Li, & Roberts, 2010). Specifically, McLaughlin and colleagues (2010) used the data from the Health and Retirement Study (HRS) and estimated the prevalence and correlates of successful agers among older Americans. The researchers showed a decline in the prevalence of successful aging over time, even after controlling for individual sociodemographic factors, such as age, gender, education, and race/ethnicity. Extending McLaughlin and colleagues’ study, Hank (2011) compared older adults in 15 European countries using the data from the Survey of Health, Aging, and Retirement in Europe (SHARE). His study revealed national differences in the prevalence of successful aging and such variations remained even after accounting for microlevel individuals’ factors. Based on the data from international comparative surveys, these studies suggest the potential role of macrolevel societal factors in aging, such as health care systems and economic conditions. However, cross-national comparison on successfully aging in Asia is rather limited, as most of the past researches were conducted in Western countries.

In Asia, most of the studies modified Rowe and Kahn’s model of successful aging by selectively focusing on certain criteria or adding new criteria (Chou & Chi, 2002; Feng, Son, & Zeng, 2015; Hsu & Jones, 2012; Ng, Broekman, Niti, Gwee, & Kua, 2009), except one study (Liu et al., 2017). Furthermore, only a handful of studies have directly investigated variations in successful aging across countries. As an exception, Feng and colleagues (2015) compared the prevalence and correlates of successful aging among older adults aged 65 years and above between China and South Korea (hereafter, Korea). They found differences within and across countries. The proportion of successful agers was smaller in China than in Korea. In addition, urban residents in China had a higher likelihood of being successful agers than their counterparts residing in rural areas. Such urban–rural differences were also shown in another study (Liu et al., 2017). However, the definition and operationalization of successful aging in these studies conducted in Asia was different from those in the studies conducted in Western countries (Hank, 2011; McLaughlin et al., 2010). More specifically, Feng and colleagues (2015) did not strictly follow Rowe and Kahn’s model of successful aging as they excluded cognitive function and added life satisfaction as a criterion of successful aging. Liu and colleagues (2017) followed Rowe and Kahn’s model, but did not include paid work as the active engagement measure. Thus, these modifications limit the direct comparison with prior studies in Western countries (Hank, 2011; McLaughlin et al., 2010). The present study therefore aimed to fill such gap.

The Present Study

As described earlier, several studies have indicated that occurrence of successful aging can vary within and across countries (Feng et al., 2015; Hank, 2011; Liu et al., 2017; McLaughlin et al., 2010). Yet, comparable evidence remains relatively sparse for non-Western countries. Extending the prior studies, the present study aimed to examine the prevalence and correlates of successful aging in three countries in East Asia (China, Korea, and Japan) based on the Rowe and Kahn’s model, using data from surveys designed to parallel the HRS and SHARE conducted in North America and Europe, respectively. Investigating country variations is crucial in order to identify the universal and country-specific societal factors influencing individuals’ aging process in late adulthood. The findings of such comparison are also helpful to provide insights for developing policy measures in dealing with challenges raised by population aging, particularly in neighboring Asian countries.

The three countries were selected for comparison because they share cultural values of respecting older adults, for instance, filial piety (Sung, 2001). However, there are substantial differences in the development of health care system, economic conditions, and education system among China, Korea, and Japan. In particular, regarding the health care systems, Korea and Japan introduced long-term care systems in 2000 and 2008, respectively, whereas the development of health care system in China was relatively slow, especially the health care services provided to rural Chinese residents (e.g., access to services and coverage by the public insurance system) were poorer than those residing in urban areas (Dong & Phillips, 2008). Several studies confirmed this discrepancy and reported that rural residence was negatively related to successful aging in China (Feng et al., 2015; Liu et al., 2017).

In terms of economic conditions, according to the annual publication of the World Bank, gross domestic product per capita in 2017 was much higher in Japan and Korea (US$38,429 and US$29,743, respectively) than in China (US$8,827). Lee et al. (2018) demonstrated that economic and demographic factors (e.g., gross domestic product and life expectancy) were associated with one of the successful aging criteria (absence of disease and disability) among females.

Lastly, the development of education systems also varied among the three countries. Japan first introduced modern systems, including higher education, in 1872, after the Meiji restoration in 1868. In Korea, although modernization proceeded rapidly following the Donghak Revolution in 1894, modern education systems were introduced when American military forces occupied the country in 1945. Since the founding of Korea in 1948, Korean education systems were reformed and established. Relative to other two Asian countries, the compulsory education was implemented in China later in 1986. Alwin and McCammon (2001) commented that historical improvements in education systems contribute to cohort differences in cognitive function within a country. In view of these variations in the development of education systems, cognitive function (one of the criteria of successful aging) of older adults could vary across these countries.

In summary, given the variations in the societal development described above, it is expected that the prevalence of successful aging would be higher in Korea and Japan than in China, particularly in its rural areas, even after controlling for individual sociodemographic factors (age, gender, and education).

Method

Data

We used harmonized data sets from three national surveys: the China Health and Retirement Longitudinal Study (CHARLS; Wave 1), the Korean Longitudinal Study of Aging (KLoSA; Wave 2), and the Japanese Study on Aging and Retirement (JSTAR; Wave 2). These surveys were modeled on HRS in the United States and SHARE in Europe. While study variables and data collection procedures were comparable across studies, there were still methodological differences across and even within studies.

Specifically, whereas CHARLS and KLoSA were nationally representative studies, the second wave of JSTAR was a population-based study employed in seven municipalities. Participants were chosen through multistage probability sampling based on administrative regions (urban communities/rural villages) in China and geographical regions (urban/rural areas) in Korea. In Japan, whereas municipalities were not randomly selected and most of them were urban communities except Shirakawa village, some municipalities (e.g., Sendai city) consisted of both urban and rural areas. The year of survey was 2011 for the first wave of CHARLS, whereas the second wave of KLoSA and JSTAR were conducted in 2009 and 2008, respectively. Face-to-face interviews were performed in CHARLS and KLoSA, while both self-administered questionnaires and interviews were utilized in the data collection of JSTAR. Furthermore, the age ranges of participants were 45 years and above in CHARLS and KLoSA, and between 50 and 75 years in JSTAR. Details on the methods of these three surveys have been described elsewhere (Ichimura, Shimizutani, & Hashimoto, 2009; Jang et al., 2009; Zhao, Hu, Smith, Strauss, & Yang, 2014).

We included respondents being 65–75 years old and providing the survey weights, and excluded those providing proxy-reports only and having missing values on the study variables. As a result, a total of 6,479 older adults were selected (2,885 from CHARLS, 2,520 from KLoSA, and 1,074 from JSTAR). Supplementary Figure 1 shows a flow chart indicating the sample selection.

Dependent Variables

Using the conceptualization and operationalization of successful aging in prior literature (Hank, 2011; McLaughlin et al., 2010; Rowe & Kahn, 1987, 1997), we defined successful aging as having no major disease, no disability in activity of daily living (ADL), obtaining a median or higher score on tests of cognitive function, and being actively engaged. We operationalized the specific four measures as follows. However, the current study did not include physical function as the criteria of successful aging, because KLoSA did not assess physical function.

No major disease

Participants were asked whether they had been ever diagnosed with any of the five following chronic diseases: cancer, chronic lung disease, diabetes, heart disease, and stroke.

We further included a measure of mental health: the Center for Epidemiologic Studies Depression scale (CES-D). The CES-D is a self-reported depression questionnaire, and its higher scores indicate greater depressive symptoms (Radloff, 1977). Although the number of items, wording, and response anchors differed across studies, we adopted cut-off points in each version of the CES-D (9 or below in China and Korea, and 15 or below in Japan; see Andresen, Malmgren, Carter, & Patrick, 1993; Boey, 1999; Jang, Kim, & Chiriboga, 2005; Shima, Shikano, Kitamura, & Asai, 1985). The differences across studies in the CES-D were described in details elsewhere (Ichimura et al., 2017).

When respondents did not report any of the five chronic diseases and obtained a score of less than the cut-off point on the CES-D, they were considered to meet the no major disease criterion and were coded as 1; otherwise 0.

No disability

Respondents who reported no difficulty in performing each of five ADLs (dressing, bathing or showering, eating, getting in and out of bed, and using the toilet) met the no disability criterion and were coded as 1; otherwise 0.

High cognitive function

Two performance tests (time orientation and serial subtraction) were used to measure cognitive function. These tests were included as subscales in a cognitive screening tool (Mini-Mental State Examination; Folstein, Folstein, & McHugh, 1975). To assess time orientation, participants answered the date of the survey (i.e., date of month, year, and day of week). The response to each item was coded as 1 for a correct answer and 0 for a wrong answer. Thus, the score ranged from 0 to 4. Regarding serial subtraction, participants subtracted 7 starting from 100. One point was added for each correct answer, and the maximum score was 5. For missing scores on each test, we computed scores of 0. We computed summary scores of the two tests, and used the grand median score as the cut-off point (i.e., 6). Respondents whose summary scores were at median or higher were considered to meet the high cognitive function criterion and were coded as 1; otherwise 0.

Active engagement

Participants were defined as actively engaged when they were currently participating in any social activities such as social clubs, community activities, volunteering, or leisure activities, and if they were engaging in paid work. Respondents who engaged in either social or productive activities were considered to meet the active engagement criterion and were coded as 1; otherwise 0. Because the variable on social activities was not available in the harmonized JSTAR data set, we extracted the variable from its preharmonized data.

Global measure of successful aging

Using the four measures described earlier, those who met all the criteria were regarded as successful agers and were coded as 1; otherwise 0. This global measure, as well as the four specific measures of successful aging described above, formed our dependent variables in this paper.

Independent Variables

We used three sociodemographic factors at the individual level, namely, age, gender, and education, as the independent variables. Gender was coded as 0 for male and 1 for female. Following the International Standard Classification of Education (UNESCO, 1997), education refers to the highest educational degree obtained (“low” = lower secondary education or below, “medium” = upper secondary or postsecondary education, and “high” = tertiary education or above). Two dummy variables were created to indicate medium or high levels of education, with a low level of education as the reference.

In order to examine variations in successful aging across countries, we included participants’ country as a factor at the societal level. For additional analyses, we included participants’ region of residence (i.e., urban/rural areas) as another societal factor. In JSTAR, however, the residence of region was not available in the harmonized data set. Besides, only a limited number of regions (two out of seven) were defined as urban areas according to the Ministry of Internal Affairs and Communications. Therefore, we examined the effect of region of residence only in China and Korea.

Missing Data

The proportions of respondents who were excluded from the full young-old samples were highest in Japan (45.0%), followed by China (14.2%) and Korea (5.8%). The reasons for the large exclusion rate in JSTAR were having missing values (39.5%), providing proxy reports only (5.0%), and unavailability of the survey weights (0.4%). Missing values were partly due to nonresponses to the cognitive tests (14.9%) and the self-reported questionnaires (11.7%). The survey weights in JSTAR considered additional variables such as employment and marital status. Therefore, when participants had missing values on those variables, the survey weights were not computed.

To evaluate missing patterns, we compared participants who were included for the analysis with those who were excluded. The differences in the study variables were minimal in CHARLS (Cohen’s d, φ, and all Cramer’s Vs < 0.10). In KLoSA, the eligible respondents were more likely to meet the disability criterion than those who were ineligible (96.4% and 84.6%, respectively; φ = 0.13). In JSTAR, participants who were included for the analysis were younger than those who were not (M = 70.22 and 70.64 years, respectively; Cohen’s d = 0.14). For other variables, there were only marginal differences between respondents who were included for the analysis and those were in KLoSA and JSTAR (Cohen’s d, φ, and all Cramer’s Vs < 0.04 and < 0.10, respectively). These results suggested that the amounts of selection bias were relatively small across countries.

Data Analysis

First, descriptive characteristics of the study sample were presented by region of residence as well as by country. Next, to examine differences within and across countries in the prevalence of successful agers, multivariate logistic regression analyses were performed. The odds of being successful agers according to countries and regions of residence were evaluated, and individual sociodemographic factors were controlled for. The results of the logistic regressions were presented as odds ratios (ORs), together with 95% confidence intervals (CIs). These analyses were weighted to correct for oversampling and nonresponse in each survey. The significance tests were evaluated at p < .001. We employed effect coding (e.g., Wendorf, 2004) to contrast three groups (China, Korea, and Japan). So, the ORs can be interpreted as the estimated differences between a specific group mean and the grand mean of all groups. As additional analyses, we also contrasted five groups (urban China, rural China, urban Korea, rural Korea, and Japan).

Results

Descriptive Analysis

Table 1 shows the sample characteristics of each country and region of residence. When focusing on country differences, the distribution of individual sociodemographic factors varied across countries. The age and the proportion of females were highest in Japan, followed by China and Korea. The educational level was highest in Japan, followed by Korea and China. There were also urban–rural differences in education in Korea and China: The educational level was higher in urban areas than in rural areas.

Table 1.

Sample Characteristics by Country and Region of Residence

Characteristics China China/urban China/rural Korea Korea/urban Korea/rural Japan
(n = 2,885) (n = 1,156) (n = 1,729) (n = 2,520) (n = 1,743) (n = 777) (n = 1,074)
M (SD) or % (n) M (SD) or % (n) M (SD) or % (n) M (SD) or % (n) M (SD) or % (n) M (SD) or % (n) M (SD) or % (n)
Age (years) 69.35 (3.11) 69.66 (3.08) 69.14 (3.10) 69.69 (3.06) 69.61 (3.06) 69.87 (3.05) 69.71 (3.11)
Gender
 Male 51.8 (1,497) 50.3 (577) 52.7 (920) 43.1 (1,106) 43.7 (765) 41.8 (341) 36.0 (532)
 Female 48.2 (1,388) 49.7 (579) 47.3 (809) 56.9 (1,414) 56.3 (978) 58.2 (436) 64.0 (542)
Education
 Low 91.7 (2,625) 83.3 (937) 97.5 (1,688) 77.7 (1,965) 72.6 (1,279) 88.8 (686) 42.1 (425)
 Medium 5.9 (181) 11.3 (146) 2.1 (35) 15.4 (378) 18.3 (306) 8.9 (72) 47.6 (516)
 High 2.4 (79) 5.5 (73) 0.3 (6) 7.0 (177) 9.1 (158) 2.3 (19) 10.3 (133)
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Note: Percentages were weighted, but means, standard deviations, and sample numbers (n) are not weighted.

Table 2 presents the percentages of participants who met specific and global measures of successful aging within and across countries. Regarding country differences, we found differential patterns in each successful aging criterion. The ratio of participants who met the disease criterion was highest in Japan, followed by Korea and China (grand mean = 43.0%). The proportion of those who met the disability criterion was highest in Korea, followed by Japan and China (grand mean = 80.6%). The ratio of those who met the cognitive function criterion was highest in Japan, followed by Korea and China (grand mean = 50.5%). The proportion of those who met the active engagement criterion was highest in China, followed by Korea and Japan (grand mean = 69.8%). Lastly, the ratio of older people meeting the global measure of successful aging was highest in Japan, followed by Korea and China (grand mean = 17.6%). Whereas 29.2% of older Japanese adults experienced successful aging, the percentages were 15.7% and 25.5% in their Chinese and Korean counterparts, respectively.

Table 2.

Percentage of Older Adults Meeting Specific and Global Measures of Successful Aging by Country and Region of Residence

Criterion China China/urban China/rural Korea Korea/urban Korea/rural Japan
% (n) % (n) % (n) % (n) % (n) % (n) % (n)
No major disease 40.4 (1,118) 42.2 (464) 39.1 (652) 48.2 (1,194) 47.2 (818) 50.3 (376) 62.1 (649)
No disability 77.9 (2,240) 83.7 (952) 73.9 (1,288) 96.4 (2,422) 96.1 (1,670) 96.9 (752) 95.6 (1,037)
High cognitive function 44.2 (1,285) 53.3 (636) 37.8 (649) 75.1 (1,900) 76.2 (1,326) 72.6 (574) 90.8 (986)
Active engagement 73.5 (2,128) 66.0 (748) 78.7 (1,380) 55.9 (1,406) 50.4 (882) 68.0 (524) 46.0 (507)
Global measure 15.7 (425) 18.1 (188) 14.0 (237) 25.5 (627) 22.8 (392) 31.1 (235) 29.2 (318)
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Note: Percentages were weighted, but sample numbers (n) are not weighted.

Next, when stratifying the Chinese and Korean samples by their regions of residence, the ratio of rural residents who met the cognitive function criterion was lower than that of urban residents. In contrast, the proportion of rural residents who met the active engagement criterion was higher than that of urban residents. These patterns were consistent in both countries. Variations in the disability criterion within a country were evident only in China: More rural residents reported disability relative to urban residents. In terms of the disease criterion and the global measure, opposite patterns of relationship were found between China and Korea. In particular, urban residents were more likely to meet these two criteria than rural residents in China, whereas urban residents were less likely to meet the criteria than rural residents in Korea.

Multivariate Analysis

Multivariate logistic regression analyses were performed on the specific and global measures of successful aging. The results of variations across countries are summarized in Table 3. Sociodemographic factors at the individual level (i.e., age, gender, and education) were entered in addition to country. First, the patterns of the age and gender effects were consistent across the five dependent variables. Being younger and males were associated with a higher likelihood of both global and specific measures of successful aging (ORs ranging from 0.91 to 0.95 for age, and from 0.32 to 0.72 for gender). Effects of education varied depending on the dependent variables. Compared with participants who had lower levels of education, those who had higher levels of education were more likely to meet the criteria of disability, cognitive function, and the global measure (ORs ranging from 1.85 to 2.34, from 4.48 to 4.72, and from 1.04 to 1.59, respectively), whereas such participants were less likely to meet the criterion of active engagement (ORs ranging from 0.73 to 0.84). An ambiguous pattern emerged for the disease criterion. Participants who had low levels of education were less likely to meet the criterion than those who had medium levels of education (OR = 1.23), but more likely to meet the criterion than those who had high levels of education (OR = 0.85).

Table 3.

Multivariate Logistic Regressions for Specific and Global Measures of Successful Aging: Differences by Country

Characteristics No major disease No disability High cognitive function Active engagement Global measure
ORs (95% CIs) ORs (95% CIs) ORs (95% CIs) ORs (95% CIs) ORs (95% CIs)
Sociodemographic factors
 Age 0.93* (0.93–0.93) 0.95* (0.95–0.95) 0.91* (0.91–0.91) 0.91* (0.91–0.91) 0.92* (0.92–0.92)
 Gender (ref.: male) 0.71* (0.71–0.71) 0.72* (0.72–0.72) 0.36* (0.36–0.36) 0.58* (0.58–0.58) 0.42* (0.42–0.42)
Education (ref.: low)
 Medium 1.23* (1.22–1.23) 2.34* (2.34–2.35) 4.48* (4.47–4.49) 0.84* (0.84–0.84) 1.59* (1.58–1.59)
 High 0.85* (0.85–0.86) 1.85* (1.84–1.86) 4.72* (4.70–4.74) 0.73* (0.73–0.73) 1.04* (1.04–1.05)
Country (effect coding)
 China 0.66* (0.66–0.66) 0.31* (0.30–0.31) 0.27* (0.27–0.27) 1.76* (1.76–1.77) 0.61* (0.61–0.61)
 Korea 0.93* (0.93–0.93) 2.22* (2.21–2.23) 1.18* (1.18–1.18) 0.85* (0.84–0.85) 1.17* (1.16–1.17)
 Japan 1.62* (1.62–1.62) 1.48* (1.47–1.48) 3.09* (3.08–3.09) 0.67* (0.67–0.67) 1.40* (1.40–1.41)
Pseudo-R2 0.037 0.076 0.263 0.089 0.081
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Note: CI = confidence interval OR = odds ratio. All models incorporate survey weights.

N = 6,479. *p < .001.

Next, differences in the specific and global measures of successful aging across countries remained significant, even after controlling for the individual sociodemographic factors. Specifically, the ratios of participants who met the criteria of disease and cognitive function were highest in Japan and lowest in China (ORs ranging from 0.66 to 1.62 for disease, and from 0.27 to 3.09 for cognitive function). Conversely, the proportion of those who met the active engagement criterion was highest in China and lowest in Japan (ORs ranging from 0.67 to 1.76). Additionally, the ratio of those who met the disability criterion was highest in Korea and lowest in China (ORs ranging from 0.31 to 2.22). With regard to the global measure of successful aging, the odds of being successful agers were highest in Japan and lowest in China (ORs ranging from 0.61 to 1.40).

The results of additional analyses examining differences within and across countries were presented in Table 4. Similar and different regional variations were identified between China and Korea. In both countries, rural residents were less likely to meet the cognitive function criterion, but were more likely to meet the active engagement criterion, relative to urban residents. In China, the ratios of individuals meeting the specific and global measures of successful aging were lower in rural areas (ORs ranging from 0.24 to 0.67), except for the active engagement criterion. In Korea, however, the proportions of individuals meeting the global and specific measures of successful aging were higher in rural areas (ORs ranging from 1.11 to 2.87), except for the cognitive function criterion.

Table 4.

Multivariate Logistic Regressions for Specific and Global Measures of Successful Aging: Differences by Country and Region of Residence

Characteristics No major disease No disability High cognitive function Active engagement Global measure
ORs (95% CIs) ORs (95% CIs) ORs (95% CIs) ORs (95% CIs) ORs (95% CIs)
Country/region (effect coding)
 China/urban 0.78* (0.78–0.78) 0.44* (0.44–0.44) 0.47* (0.47–0.47) 1.15* (1.15–1.15) 0.75* (0.74–0.75)
 China/rural 0.67* (0.67–0.67) 0.24* (0.24–0.25) 0.26* (0.25–0.26) 2.08* (2.08–2.08) 0.53* (0.53–0.53)
 Korea/urban 0.96* (0.96–0.97) 2.03* (2.03–2.04) 1.46* (1.45–1.46) 0.59* (0.59–0.59) 1.00* (0.99–1.00)
 Korea/rural 1.11* (1.11–1.11) 2.87* (2.84–2.89) 1.42* (1.42–1.43) 1.29* (1.29–1.30) 1.71* (1.70–1.71)
 Japan 1.79* (1.79–1.79) 1.80* (1.80–1.81) 4.02* (4.01–4.03) 0.55* (0.55–0.56) 1.48* (1.48–1.49)
Pseudo-R2 0.039 0.092 0.280 0.108 0.086
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Note: CI = confidence interval; OR = odds ratio. All models incorporate survey weights. Sociodemographic factors at the individual level (age, gender, and education) are controlled for, but the results are not presented.

N = 6,479. *p < .001.

Discussion

Using data from international comparative surveys in China, Korea, and Japan, the present study compared the prevalence and correlates of successful aging in the three aging countries in East Asia based on the model proposed Rowe and Kahn (1987, 1997). Given variations in societal development among these countries, we predicted that the prevalence of successful aging would be higher in Korea and Japan than in China, especially in its rural areas. In partial support of our prediction, the findings showed that the prevalence estimate of successful aging was highest in Japan (29.2%), followed by Korea (25.5%) and China (15.7%). These country variations in the prevalence of successful agers remained even after controlling for the three sociodemographic factors at the individual level, including age, gender, and education. Successful agers were often those who were younger and males. The ratio of successful agers was higher in individuals with higher levels of education compared with those with lower education. These results indicated the potential role of macrolevel societal factors, over and above microlevel factors, in determining individuals’ opportunities for successful aging in East Asia.

Overall, the present findings are largely consistent with previous findings (Feng et al., 2015; Hank, 2011; Liu et al., 2017; McLaughlin et al., 2010), revealing that the prevalence of successful agers varied within and across countries. It is important to note that unobserved factors at the societal level (e.g., cultural values, health care policies, and economic conditions) would affect individuals’ aging process. Indeed, older females in more developed countries were diagnosed with fewer diseases and reported fewer disabilities than their counterparts in less developed countries (Lee et al., 2018). Future research should directly assess these macrolevel factors to unravel the impact of societal contexts on physical, cognitive, and social function in late life.

However, variations in measurements across countries restricted a direct comparison of our results with those shown in Western countries (Hank, 2011; McLaughlin et al., 2010). For example, unlike the previous studies, physical function was not included as the successful aging criterion in the present study, and the coverage of cognitive function and active engagement measurements was narrower. Specifically, physical function was not assessed in Korea. Besides, the word recall test was not included in the cognitive function criterion because its procedure in KLoSA was different from that in CHARLS and JSTAR. In terms of the active engagement criterion, while providing care to family (e.g., grandchildren) was included in the Western investigations (Hank, 2011; McLaughlin et al., 2010), such family-related activities were not always taken into account in the current and earlier studies in Eastern countries (Feng et al., 2015; Ng et al., 2009). Given that older adults are traditionally respected in East Asia (Sung, 2001), our active engagement criterion might underestimate older individuals’ active role in family. These differences in measurements should be acknowledged when comparing our findings with those shown in the Western studies. Yet, there are two successful aging criteria, namely, physical function and cognitive function (with the word recall test being added to this criterion for an additional analysis), which were measured in CHARLS, JSTAR, HRS, and SHARE, allowing a direct comparison between our findings and those reported in the previous research. As a result, country and regional differences remained largely the same as those reported in the present study (Supplementary Tables 1 and 2). Additionally, when using the country-specific medians for the cognitive function criterion, the prevalence of successful aging was higher in Japan (17.4%) but lower in China (13.6%) compared with that in the United States (15.7% in the young-old in 2004; Table 2 in McLaughlin et al., 2010).

We also found inconsistent results from earlier studies in Eastern countries: While Feng and colleagues (2015) did not observe urban–rural differences in successful aging in Korea, our study showed that rural Korean residents were more likely to be successful agers than their urban counterparts. This inconsistency should be interpreted with caution. In particular, the definition of urban/rural areas differed between China and Korea. In KLoSA, relatively large cities were categorized as rural areas because of their geographical locations. Even so, one possible explanation could be migration from rural to urban areas. Older rural residents were likely to move to urban areas due to limited medical services and unemployment (Park & Kim, 2016). These migrants therefore could exhibit poor health status and suffer from financial burden of medical treatment. Future studies should investigate migrants’ health status to obtain a more comprehensive picture of the urban–rural differences in Korea.

Furthermore, there were significant variations in the specific criteria of successful aging within and across countries. Although the odds of being successful agers were highest in Japan and lowest in rural China, an opposite pattern emerged for the active engagement criterion. In China, the rise of off-farm work and increased migration from rural to urban areas alter family structures, especially families in rural areas (Pang, Brauw, & Rozelle, 2004). Thus, older adults living in the rural areas might have to continue to work after their children relocated to urban areas, resulting in the higher ratio of rural Chinese residents with paid work (ranging from 20.5% to 63.3%). On the other hand, participation in social activities was least frequent in Japan (ranging from 17.0% to 49.0%). It might be a challenge for older Japanese adults to engage in informal or community activities after retirement. To clarify such mechanisms, family networks and financial status (e.g., pensions and savings) should be further considered.

Limitations and Future Directions

The three surveys in East Asia were designed to be comparable with HRS and SHARE in North America and Europe, respectively. Yet, there were still methodological variations across studies, as described below.

First, the years of survey and sampling frames varied across studies. The years of survey were 2011 in China, 2008 in Korea, and 2009 in Japan. Given that the economic recession of 2008 caused increases in depressive symptoms among older Americans (Pruchno, Heid, & Wilson-Genderson, 2017), the prevalence of successful aging in East Asia might also vary depending on the year of assessment. Regarding the sampling frames, unlike the other two databases (CHARLS and KLoSA) which recruited participants from both urban and rural areas, JSTAR mostly represented urban areas, thus limiting the comparison with the effects of residence shown in Chinese and Korean samples. Given a limited number of rural administrative regions in JSTAR, we did not stratify the Japanese sample by their area of residence in the additional analyses. However, it is worth to note that, in the Japanese long-term care system, assessment criteria and service availability varied across regions (Tsutsui & Muramatsu, 2005, 2007). Thus, the prevalence and correlates of successful aging shown in the present Japanese sample may not be generalized to rural areas in Japan. Furthermore, due to the narrower age range in JSTAR, Chinese and Korean participants aged 76 years and older were excluded from the current study. Additional analyses showed that the prevalence of successful agers was lower in the old-old compared with the young-old (Supplementary Table 3). More work is needed to investigate country differences among very old populations.

Second, the validity of our study measures should be carefully considered. Using data from CHARLS, an earlier study suggested that diabetes prevalence in rural areas was lower than in urban areas, in part, due to limited access to medical services (Zhao et al., 2016). Thus, the prevalence of chronic diseases might be underestimated in rural China. Indeed, when considering hypertension and arthritis, which were available in the harmonized data sets but were not included as the disease criterion in previous studies, urban–rural differences in China were minimized or even reversed (Supplementary Table 4).

Third, Rowe and Kahn’s (1987, 1997) conceptualization of successful aging has been criticized for its narrow criteria deeply rooted in the predominant value of Western, white, and middle-class men (Martinson & Berridge, 2015). Empirically, several studies explored culturally specific meanings of successful aging in Latin America as well as in Asia (e.g., Feng & Straughan, 2017; Fernández-Ballesteros et al., 2008; Iwamasa & Iwasaki, 2011). In future studies, cultural meanings should be added to the objective measures of successful aging, for better understanding of successful aging in different countries. Also, cross-national comparison should be expanded to other countries, such as Latin America and the Caribbean, as well as other Asian countries.

Conclusion

Using internationally comparable data to three countries in East Asia (China, Korea, and Japan), this study uniquely adds prevalence and correlates of successful aging to previous findings in North America and Europe. Our results revealed differences in the prevalence of successful agers within and across countries, even after controlling for individual sociodemographic factors such as age, gender, and education. The likelihood of being successful agers was highest in Japan and lowest in China, particularly in rural areas. Though when looking into the specific criteria of successful aging across countries, such as active engagement, opposite patterns were shown: Chinese were more actively engaged than Koreans and Japanese. These results indicate that both global and specific criteria should be utilized in future research on successful aging. Furthermore, future examinations should explore potential macrolevel factors underlying variations in successful aging within and across countries. At the same time, more policy attention should be devoted to facilitating successful aging among older adults across the world.

Supplementary Material

gbaa042_suppl_Supplementary-Material
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Acknowledgments

The authors used data and information from the Harmonized CHARLS data set and Codebook, Version C as of April 2018; the Harmonized KLoSA data set and Codebook, Version B as of November 2015; and the Harmonized JSTAR data set and Codebook, Version B as of August 2014, which were developed by the Gateway to Global Aging Data. The development of the Harmonized CHARLS, KLoSA, and JSTAR was funded by the National Institute on Ageing (R01 AG030153, RC2 AG036619, 1R03AG043052). For more information, please refer to www.g2aging.org. The authors also thank the research teams in the three countries for data collection: the China Centre for Economic Research at Beijing University; the Korean Labor Institution; and the Research Institute of Economy, Trade and Industry (RIETI), Hitotsubashi University, and the University of Tokyo.

Funding

This work was supported by Japan Society for the Promotion of Science KAKENHI (grant number 18J00674).

This paper was published as part of a supplement with funding from the National Institute on Aging (R01AG030153).

Conflict of Interest

None reported.

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