The role of family support in the well-being of older people: Evidence from Malaysia and Viet Nam

Rodgers, Yana; Zveglich, Joseph E.; Ali, Khadija; Xue, Hanna Working Paper The role of family support in the well-being of older people: Evidence from Malaysia and Viet Nam ADB Economics Working Paper Series, No. 730 Provided in Cooperation with: Asian Development Bank (ADB), Manila Suggested Citation: Rodgers, Yana; Zveglich, Joseph E.; Ali, Khadija; Xue, Hanna (2024) : The role of family support in the well-being of older people: Evidence from Malaysia and Viet Nam, ADB Economics Working Paper Series, No. 730, Asian Development Bank (ADB), Manila, https://doi.org/10.22617/WPS240325-2 This Version is available at: https://hdl.handle.net/10419/299305 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/3.0/igo/ ASIAN DEVELOPMENT BANK ASIAN DEVELOPMENT BANK 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org ADB ECONOMICS WORKING PAPER SERIES NO. 730 June 2024 The Role of Family Support in the Well-Being of Older People Evidence from Malaysia and Viet Nam Rapid demographic changes in Malaysia and Viet Nam could disrupt traditional family support for older people. An analysis of unique panel data from the Malaysia Ageing and Retirement Survey and the Viet Nam Aging Survey points to the benefits of living conditions—including marital status and whether one’s children live nearby—for the physical and mental well-being of older people. Given the estimated protective effect of living arrangements examined in the paper, governments may need to adjust social safety nets to bolster the physical and mental health of senior citizens living alone. About the Asian Development Bank ADB is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 68 members —49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. THE ROLE OF FAMILY SUPPORT IN THE WELL-BEING OF OLDER PEOPLE EVIDENCE FROM MALAYSIA AND VIET NAM Yana van der Meulen Rodgers, Joseph E. Zveglich, Jr., Khadija Ali, and Hanna Xue ASIAN DEVELOPMENT BANK The ADB Economics Working Paper Series presents research in progress to elicit comments and encourage debate on development issues in Asia and the Pacific. The views expressed are those of the authors and do not necessarily reflect the views and policies of ADB or its Board of Governors or the governments they represent. ADB Economics Working Paper Series Yana van der Meulen Rodgers, Joseph E. Zveglich, Jr., Khadija Ali, and Hanna Xue No. 730 | June 2024 Yana van der Meulen Rodgers ([email protected]) is a professor of Labor Studies and Employment Relations and faculty director of the Center for Women and Work and Hanna Xue ([email protected]) is a PhD candidate at Rutgers University. Joseph E. Zveglich, Jr. ([email protected]) is the deputy chief economist at the Economic Research and Development Impact Department and Khadija Ali ([email protected]) is an operations analyst at the Central and West Asia Department, Asian Development Bank. The Role of Family Support in the Well-Being of Older People: Evidence from Malaysia and Viet Nam Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2024 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 8632 4444; Fax +63 2 8636 2444 www.adb.org Some rights reserved. Published in 2024. ISSN 2313-6537 (print), 2313-6545 (PDF) Publication Stock No. WPS240325-2 DOI: http://dx.doi.org/10.22617/WPS240325-2 The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies ofthe Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. 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If the material is attributed toanother source, please contact the copyright owner or publisher of that source for permission to reproduce it. ADB cannot be held liable for any claims that arise as a result of your use of the material. Please contact [email protected] if you have questions or comments with respect to content, or if you wish toobtain copyright permission for your intended use that does not fall within these terms, or for permission to use theADB logo. Corrigenda to ADB publications may be found at http://www.adb.org/publications/corrigenda. Note: ADB recognizes “China” as the People’s Republic of China, “Korea” as the Republic of Korea, “Vietnam” as Viet Nam, and “Hanoi” as Ha Noi. 1 ABSTRACT Demographics in Malaysia and Viet Nam are evolving rapidly, potentially disrupting traditional family support to older people. We estimate a set of Poisson random effects models with panel data from the Malaysia Ageing and Retirement Survey and the Viet Nam Aging Survey to analyze how living arrangements, marital status, and support from children influence the mental and physical health of older people. In Malaysia, having living children plays an important protective role for both mental and physical health, while living with a son appears to have a protective effect for physical health. Results are similar for Viet Nam, except older women, who are at greater risk of mental and physical health problems, appear to enjoy a greater protective effect for their mental health from a child living nearby than do men. Our analysis underscores the importance of social safety nets for the health of senior citizens living alone. Keywords: mental health, well-being, physical health, depression, gender, women, aging JEL codes: I14, J16, O53 ______________________ The authors thank Aiko Kikkawa, Minhaj Mahmud, Maki Nakajima, Donghyun Park, participants in the 2023 Asian Development Bank workshop on the State of Well-Beings of Older Persons in Asia, and participants in the 2024 Asian Development Bank Economists’ Forum for their helpful suggestions. Guidance from Norma Mansor on the use of the Malaysia data and from Long Thanh Giang on the use of the Viet Nam data was greatly appreciated. 1 I. Introduction The number of older Asians aged 65 and above is growing rapidly. By the year 2050, the share of Asia’s older people in the total population will be 18%, exceeding the global average of 16% (Asian Development Bank [ADB] 2017). Although developing countries in the region still have relatively young populations, their demographic transitions are progressing rapidly, with a considerable shift in the age structure toward older people. This shift has prompted legislative reforms to build and reinforce the social safety net to better support older people, especially those living in poverty. A growing body of research is examining how the well-being of older women and men is influenced by their living arrangements. Across countries, families are using their own unpaid labor, especially that of women, to provide care for older family members (Stark 2005). Unpaid care for older men is often provided by spouses, while for women it is provided by other family members, especially by daughters, as documented for the Republic of Korea (Yoon 2014) and the People’s Republic of China (Chen et al. 2018). One outcome of these informal care arrangements is lower hospitalization rates for older people living with family members compared with the older people who live alone (González-González et al. 2011). In what has been called the feminization of later life, the population share has become increasingly female among older age groups in most countries, with women having higher life expectancies than men on average because of a combination of biological, social, and behavioral reasons (Kinsella 2000). Given women’s longer life expectancies, in most countries relatively more women are widowed than men, leaving women more reliant on formal services outside of the home for their healthcare needs, including long-term care. 2 Despite their longer life expectancies, women are more likely than men to report health problems and to have worse self-reported health status (Atchessi et al. 2018; Madyaningrum, Chuang, and Chuang 2018; Case and Paxson 2005). Older women are more vulnerable to poverty given their relatively higher rates of widowhood as well as insufficient support from pensions and social security owing to their relatively shorter times in the labor force (Smeeding and Sandstrom 2005). In turn, poverty and income constraints contribute to problems in accessing healthcare. Older women have faced additional constraints, including relatively lower rates of health insurance coverage, less education, and lack of economic independence, that have limited their access to healthcare and their ability to pay for it (Brinda et al. 2015, Zhang et al. 2017). Gender discrimination and class bias against poor people may also play a role causing women to slip through the cracks in gaining access to services from network hospitals (Karpagam, Vasan, and Seethappa 2016). We contribute to this literature by examining factors that are associated with the well-being of older people in Southeast Asia, with a focus on the role of family support and living arrangements. We study two countries that are experiencing pronounced demographic transitions and evolving challenges faced by the older members of their populations: Malaysia and Viet Nam. Our goal is to determine how support from children influences the mental and physical health of older people, and how these effects differ by ethnicity and gender. A focus on differences across ethnic groups reveals how people with varying ethnic backgrounds view the role of family in caring for aging parents. This approach is motivated by qualitative evidence that older Chinese in Malaysia are not supported by their children and are placed into nursing homes instead (Tey et al. 2016). 3 Moreover, the analysis will also show how discrimination by ethnicity—similar to the case of gender bias—may be reflected in disparities in the well-being of older people, to the extent that some ethic groups in these countries have experienced bias and marginalization in the labor market. Cultural nuances and different kinship systems can play a major role in influencing how older parents respond to care by their children, as evidenced by previous research indicating that the psychological well-being of older people in Viet Nam is positively associated with living with a married son but not with a daughter, while in Thailand, coresidence with a child has beneficial effects for the psychological well-being of older people regardless of the gender of the child (Teerawichitchainan, Pothisiri, and Long 2015). Our objective is to build on this research along several dimensions: we look at three measures of well-being, we use more recent data for Viet Nam and new panel data for Malaysia, and we explore how the relationship between living arrangements and the well-being of older people varies by gender and ethnicity. II. Background A. The Demographic Transition Malaysia is still in the early stages of the demographic transition while Viet Nam is (somewhat surprisingly, considering its still relatively low income) at a more advanced stage. In fact, Viet Nam is one of countries of the Association of Southeast Asian Nations that is widely cited as being at risk of getting old before getting rich (Huong 2017). Therefore, the demographic profiles of Malaysia and Viet Nam are quite different in terms of the speed of population aging. Figure 1 depicts population pyramids for the years 1990 4 and 2021 in which the bars indicate the percentage of the total population that is male or female and belongs to a particular age group. Both countries have clearly undergone pronounced demographic transitions since 1990, with a large shift in the population distribution from children to adults. Consistent with other middle-income countries in the region, the population is now concentrated among age groups considered to be young and working-age adults, and Viet Nam has started to see that shift in the distribution of the population toward older people. Strikingly, gender imbalances are progressively skewed toward women in older age groups, especially in Viet Nam. Insert Figure 1 here Looking at the data from another angle shows that most of the older people are women. Figure 2 shows the male–female sex ratio by 5-year cohorts for both countries; that is, the ratio of the number of males in each 5-year age group to the number of females, expressed as a percentage. Numbers close to 100 indicate that the shares of males and females in an age group are roughly the same. These ratios exhibit a marked and sharp drop for the older population groups, especially after the age of 50, and especially in Viet Nam. Overall, among older people, women make up the majority share for all age groups, and the difference is particularly stark for those aged 70 and above. Insert Figure 2 here B. Aging and Family Support in Malaysia A small but growing body of research has examined the essential role of social support for both physical and mental health outcomes for older people, especially concerning depression and physical inactivity (Sazlina et al. 2012, Marthammuthu et al. 11 Figures 3–6 compare several key variables between the two countries. As shown in Figure 3, 57% of older people in Viet Nam report having at least two chronic physical health conditions, considerably higher than Malaysia (32%); only 18% in Viet Nam were not diagnosed with any chronic conditions compared to 46% in Malaysia. Older people in Viet Nam are also more likely to report having depressive symptoms: 57% of older people in Viet Nam reported experiencing at least three depressive symptoms compared to 18% in Malaysia. Also of note are considerable gender differences in marital status in both countries: older men are considerably more likely to still be married compared to older women. In contrast, older women are much more likely to be widowed in both countries, especially in Viet Nam—presumably as a long-term repercussion of the loss of men’s lives during the Viet Nam war (Figure 4). In both countries, the vast majority of older people live with other family members (85% in Malaysia and 72% in Viet Nam) (Figure 5). Gender differences in living arrangements are less pronounced in Malaysia than they are in Viet Nam, where 67% of older men live with other family members compared with 74% of older women. Also in both countries, older women are more likely than older men to live by themselves, while the opposite is true for living only with one’s spouse. A final point of interest in the descriptive statistics is the location of the nearest living child. A large proportion of older people in both countries live with their children: in Malaysia, 77% of older people live with at least one child, and in Viet Nam that share is 65% (Figure 6). In Malaysia, more older people live with a son than with a daughter, and this is true for both men and women. Insert Figures 3-6 here 12 IV. Estimation Results A. Malaysia Table 1 shows the results for the factors that are associated with the number of depressive conditions in Malaysia. The table reports Poisson coefficients, each of which is interpreted as the expected change, on a log scale, the count of depressive conditions for a one-unit change in the predictor variable (holding the other predictor variables constant). Insert Table 1 here Looking first at the results for the overall sample in column 1 and the two age-group subsamples (aged 40–59 and aged 60+) in columns 2–3, we see that increasing age is associated with a somewhat higher expected number of depressive symptoms, but gender has no statistically significant association with depressive symptoms. Columns 2 and 3 show that the positive association between age in years and depressive conditions is coming entirely from the older people aged 60 and above. Among the ethnic groups in the overall sample, compared to the other Bumiputra category, being Indian is associated with more depressive symptoms, and this is driven mostly by the those aged 40–59. In contrast, being Chinese is associated with fewer depressive conditions for both age groups and for the overall sample. This result is consistent with previous findings in the literature that among older people in Malaysia, Chinese individuals are less likely to self-report poor health compared to Malays, Indian, and Indigenous People, and this result holds across income groups (Teh, Tey, and Ng 2014a; Chan et al. 2015; Khan and Flynn 2016). A possible reason is that older Chinese adults 13 exhibit more health-promoting behaviors than older Malay and Indian adults (Mohd et al. 2022). Our key independent variables for the number of children and living arrangements yield some meaningful results. As shown in column 1, compared to adults with no living children, adults who have at least one living child have a substantially lower expected number of depressive symptoms, and this is particularly true for adults who have three or four living children. This protective effect of having living children is only true for older people aged 60 and above. Closely related, compared to adults who live alone, adults who live with their spouse or with other family members have a lower expected number of depressive symptoms, and this is especially true for adults who live only with their spouse. Again, columns 2 and 3 show that these living arrangement variables only have statistically significant coefficients for older people aged 60 and above. Although it does not appear to matter if the adult is living with a son or daughter in the same home, it does matter for mental health to have a daughter living nearby, but in an unexpected way: living near one’s daughter is associated with a higher number of depressive symptoms. This result is driven by older people aged 60 and above. Closely related to living arrangements is marital status: adults who are separated or divorced have more depressive symptoms compared to people who are married in both age-group samples. Being widowed is also associated with more depressive symptoms, but this result is explained mostly by adults aged 40–59. Having never been married also appears to have an adverse mental health effect for adults aged 40–59 but a protective effect for older people aged 60 and above. Results for the other independent variables in columns 1–3 are as expected. More education is generally associated with fewer depressive conditions compared to having 14 no education at all, and the magnitude of this relationship is larger the higher the educational attainment. In addition, living in an urban area is associated with a lower number of expected depressive conditions, although the opposite is true for living in Peninsular Malaysia where most of Malaysia’s population lives. Columns 4–6 show how these results for the two age-group samples differ by gender of the respondent. Of note, the increase in depressive conditions associated with age is particularly strong for women aged 60 and beyond, while the protective effect of being Chinese holds for both men and women (except that the coefficient is not statistically significant for men aged 40–59). The results for the number of living children and living arrangements are also striking. The protective effects of having living children compared to having no children for older people hold for both men and women, as do the beneficial effect of living with one’s spouse only. However, only older men seem to experience the adverse effect for mental health from living near a daughter. Results for the number of chronic health conditions for Malaysia are in Table 2. Looking first at the overall results and the two age-group subsamples in columns 1–3, we see that being a woman is associated with a higher expected number of chronic health conditions for older people aged 60 and above, but not for younger adults. In contrast, an additional year of age is linked with more chronic conditions for adults aged 40–59, but not for older people. Among the ethnic groups, the largest effect is found for being Indian, which is associated with a fairly large increase in the expected number of chronic conditions among adults in their 40s and 50s, but not older people. Ethnic Malays in their 40s and 50s also have more chronic conditions, while the opposite is true for the “other” ethnic category. Again, these results for the different ethnic groups are largely in line with 15 previous studies on self-reported health among Malaysia’s different ethnic groups (Teh, Tey, and Ng 2014a, Chan et al. 2015, Khan and Flynn 2016). Insert Table 2 here Among the results for the number of children and living arrangements, having at least one child is positively linked with the number of chronic conditions, and this effect is coming entirely from older people aged 60 and above. The general living arrangements do not appear to play much of a role, but living with or near one’s children does. Living near one’s daughter is associated with a higher number of chronic conditions, especially for adults aged 40–59, while living with one’s son is associated with fewer chronic conditions, especially for older people aged 60 and above. Because these estimates are associations and not causal effects, it could be that daughters are likely to live close to their parents if their parents are in poor health with multiple chronic conditions, but the daughters’ own childcare arrangements prevent them from living with their parents. For older people, and especially older men, living with a son has a clear protective effect for physical health. Being widowed is also associated with having a higher number of chronic conditions. As for the other control variables, the most notable finding is the lack of a consistent protective effect from educational attainment, which is counter to the results we found for depressive conditions. As for gender differences in Columns 4–6 of Table 2, there are several key findings. First, only women in their 40s and 50s, not men, report a positive association between being Malay and having more chronic health conditions. In contrast, the positive association between having at least one child and having more chronic health conditions among older people aged 60 and above holds more for men than it does for women. A 16 similar conclusion applies to living near one’s daughter: among adults aged 40–59, the positive association between living near one’s daughter and number of chronic conditions is considerably larger for men than for women. Being widowed also has a stronger positive association with chronic conditions for men than women among older people aged 60 and above. B. Viet Nam Results for the predictors associated with the number of depressive conditions in Viet Nam are in Table 3. Overall, results for adults aged 60 and above are very similar to results for the overall sample, so compared to Malaysia, there is far less nuance by age in Viet Nam, which may be partly accounted for by differences in minimum ages in the two countries’ samples. Overall, and among older people above the age of 60, being a woman is associated with having a higher number of depressive symptoms, and the magnitude of this association is fairly large compared to many other estimates. However, being part of the ethnic majority (Kinh) has a protective effect on mental health. Among the estimates for the number of children and living conditions, having at least one child is associated with a lower number of depressive symptoms for both men and women, and the same is true for living with their spouse or living with other family members, as compared to living alone. Compared to not living close to any children, living with a child or living near a child also appears to have a protective effect on mental health for all adults and for older people. Insert Table 3 here There are more variations by age and gender for marital status. In particular, being separated or divorced is associated with a lower expected number of depressive 17 symptoms for the overall sample but a higher number of depressive symptoms for older people aged 60 and above. Most of that effect is coming from older women. Also contributing to having more depressive symptoms for women but not for men is being widowed. In direct contrast, never having been married is associated with more depressive symptoms for men and fewer depressive symptoms for women. Also of note is the protective effect of having higher educational attainment on the mental health of both older men and older women; we saw a similar result for Malaysia. Moreover, the protective effect of living with or near a child appears to only hold for women, as men have more depressive symptoms if they are living with or near a child. Finally, Table 4 reports the results for the number of chronic health conditions in Viet Nam. As we saw with the results for depression, being a woman is associated with a higher expected number of chronic health conditions than men. Age and being ethnic Kinh also have a positive association with the number of chronic health conditions. In contrast, living with or near a child is strongly associated with fewer chronic health conditions for all adults and for older people. Insert Table 4 here The results for support from family members show more differentiation by age and gender. In the overall sample, having at least one child is associated with a higher number of chronic conditions, and this result is driven primarily by men. Among adults in their 60s and above, the relationship is less clear-cut and varies by the number of children and by the gender of the respondent. So, for example, having at least one child is associated with a meaningful increase in the expected number of chronic conditions for older men, and this risk varies with the number of children, but for older women, the relationship is 18 smaller and in one case (having 3–4 children) it even becomes negative. As for living arrangements, for the most part, there is a protective effect from living with their spouse only or with other family members. This holds for everyone except for older women aged 60 and above, for whom living with their spouse is associated with more chronic health conditions. Moreover, the apparent protective effect for physical health from living with or near a child is larger for men than women. Men and women differ considerably in how marital status is associated with the number of chronic conditions. While widowhood and being separated or divorced are associated with fewer chronic health conditions among older men in their 60s and beyond, the association becomes positive for older women. However, never having been married appears to be good for one’s physical health for both older men and older women. The final noteworthy result is that the protective effect we saw in the case of mental health does not appear to hold for chronic physical conditions. Having secondary or tertiary schooling is associated with more chronic conditions for the overall sample and the sample of older people, and it holds for both men and women except for the case of older women having tertiary education. V. Conclusion This study has explored factors that are associated with health outcomes of older people in Malaysia and Viet Nam. We used recent health surveys to estimate Poisson random effects regressions to model the determinants of well-being in old age, with a focus on mental health and physical health. Our main findings for Malaysia point to a greater incidence of self-reported chronic health conditions among older women relative 19 to older men. Consistent with earlier studies, we also find substantial differences across ethnic groups in the likelihood of reporting both depressive symptoms and chronic health conditions, with Indian adults being more likely to report mental and physical health issues compared to Chinese adults. Having living children plays an important protective role for both mental and physical health compared to having no children at all, especially for older people aged 60 and above. Living with a son also appears to have a protective effect on physical health. As expected, living alone and being widowed or separated or divorced are all associated with more depressive symptoms. The results for Viet Nam are similar, except that older women are at greater risk of both mental health and physical health problems compared to men, and women in Viet Nam enjoy more of a protective effect for their mental health from living with or near a child than do men. On balance then, family support is critical for the mental and physical health of older people in both countries, with the implication that senior citizens who are living alone need extra support through the social safety net. In Malaysia, the government has taken several steps to promote the health and well-being of older people. Notably, Malaysia is one of just a few Asian countries that has achieved universal health coverage, meaning that people receive needed healthcare services without experiencing financial hardship (Kowal, Ng, and Hoang 2024). Another key measure in place includes the National Policy on Aging, which covers various aspects of older people’s well-being, including healthcare, social support, and financial security. The government has been working to increase the accessibility and affordability of healthcare services, as well as providing specialized services for age-related conditions. The government has also conducted health education and promotion campaigns 20 targeting older people to raise awareness about healthy living, disease prevention, and the importance of regular health check-ups. In terms of financing healthcare, the government provides financial support to older people through programs like the Social Welfare Department’s financial aid schemes, which aim to assist those with low incomes. Such efforts have not only come from the top down, as initiatives have been implemented to provide community-based care for older people, which allows them to receive healthcare services and support in their own communities, thus helping to reduce the need for institutionalized care. In addition, various social support programs and initiatives have been launched to address the social and emotional well-being of older people, including social clubs, recreational activities, and counseling services. More recent efforts include implementing and enforcing regulations on elder care services in Malaysia to ensure that older people receive appropriate care and support. These various initiatives reflect the government’s recognition of the aging population and the need to focus on care. In early 2023, the government proposed a comprehensive policy solution to address its aging population. One of the most striking provisions of the Senior Citizens’ Bill is its financial penalties for people placing their older parents into formal care institutions, highlighting the continued role of filial piety customs in Malaysian culture by attempting to enforce a statutory duty that legally binds adult children to caring for their older parents (Hui 2023). However, critics point to the 2023 and 2024 national budgets as evidence that the Government of Malaysia lacks a strong focus on responding to the needs of its aging population (New Straits Times 2023, Thomas 2023). The Malaysian Coalition on Aging has continued to urge the government to do more to support the healthy and active aging of older people, emphasizing limited savings of older people 27 Figure 5. Living Arrangements by Country Source: Authors’ calculations using Malaysia Ageing and Retirement Survey data and Viet Nam Aging Survey data. 28 Figure 6. Location of Nearest Living Child by Country Source: Authors’ calculations using Malaysia Ageing and Retirement Survey data and Viet Nam Aging Survey data. 29 Table 1. Poisson Random Effects Estimates for the Number of Depressive Symptoms, Malaysia Variable/Statistic All Age d 40 – 59 Age d 60+ Men Age d 40 – 59 Men Age d 60+ Women Age d 40 – 59 Women Age d 60+ Woman -0.023 -0.049 0.030 (0.028) (0.039) (0.041) Age (years) 0.003 ** 0.003 0.016 *** 0.001 0.010 ** 0.009 * 0.021 *** (0.002) (0.004) (0.003) (0.006) (0.004) (0.005) (0.004) Ethnicity (reference: other Bumiputra) Malay -0.076 -0.103 -0.034 0.014 -0.130 -0.209 0.039 (0.074) (0.100) (0.108) (0.156) (0.166) (0.133) (0.141) Chinese -0.342 *** -0.372 *** -0.339 *** -0.120 -0.359 ** -0.599 *** -0.342 ** (0.072) (0.099) (0.103) (0.153) (0.158) (0.134) (0.137) Indian 0.185 ** 0.223 * 0.141 0.366 * 0.025 0.104 0.233 (0.090) (0.124) (0.128) (0.196) (0.203) (0.163) (0.165) Others -0.071 -0.163 * 0.090 -0.187 -0.322 * -0.212 * 0.261 ** (0.069) (0.091) (0.101) (0.139) (0.165) (0.124) (0.132) Education (reference: less than primary) Primary -0.141 *** -0.077 -0.140 *** -0.050 -0.205 ** -0.114 -0.104 * (0.042) (0.082) (0.050) (0.148) (0.099) (0.099) (0.058) Secondary -0.370 *** -0.333 *** -0.348 *** -0.223 -0.451 *** -0.401 *** -0.281 *** (0.045) (0.080) (0.060) (0.139) (0.104) (0.096) (0.076) Tertiary -0.644 *** -0.662 *** -0.519 *** -0.732 *** -0.584 *** -0.618 *** -0.533 *** (0.081) (0.113) (0.126) (0.173) (0.161) (0.147) (0.206) Marital status (reference: married) Never married 0.003 0.298 ** -0.496 *** 0.774 *** -0.375 ** -0.291 * -0.547 *** (0.091) (0.127) (0.122) (0.173) (0.177) (0.160) (0.160) Widowed 0.207 *** 0.407 *** 0.056 0.317 * 0.181 ** 0.388 *** 0.002 (0.040) (0.076) (0.047) (0.180) (0.079) (0.085) (0.059) Separated/ divorced 0.447 *** 0.499 *** 0.384 *** 0.569 *** 0.613 *** 0.472 *** 0.259 * (0.064) (0.079) (0.118) (0.117) (0.174) (0.101) (0.156) Urban -0.110 *** -0.119 *** -0.097 *** -0.055 -0.107 ** -0.160 *** -0.086 * (0.025) (0.036) (0.035) (0.054) (0.052) (0.048) (0.047) Peninsular Malaysia 0.304 *** 0.357 *** 0.250 *** 0.259 ** 0.275 *** 0.421 *** 0.235 *** (0.044) (0.063) (0.059) (0.102) (0.090) (0.080) (0.078) Continued on the next page 30 Table 1 (continued) Variable/Statistic All Aged 40 – 59 Aged 60+ Men Aged 40 – 59 Men Aged 60+ Women Aged 40 – 59 Women Aged 60+ Living children (reference: none) 1–2 -0.256 *** 0.064 -0.693 *** 0.352 ** -0.615 *** -0.131 -0.719 *** (0.075) (0.108) (0.102) (0.166) (0.142) (0.127) (0.137) 3–4 -0.322 *** -0.036 -0.686 *** 0.249 -0.550 *** -0.230 * -0.779 *** (0.076) (0.113) (0.103) (0.178) (0.144) (0.135) (0.138) 5+ -0.278 *** 0.122 -0.760 *** 0.486 ** -0.635 *** -0.135 -0.828 *** (0.082) (0.124) (0.109) (0.192) (0.148) (0.149) (0.149) Living arrangements (reference: live alone) Spouse only -0.278 *** -0.164 -0.378 *** -0.148 -0.291 ** -0.159 -0.410 *** (0.068) (0.121) (0.080) (0.164) (0.128) (0.175) (0.110) Other family members -0.091 * -0.119 -0.111 * -0.117 -0.032 -0.109 -0.148 * (0.054) (0.093) (0.067) (0.116) (0.114) (0.146) (0.084) Nearest living son (reference: living elsewhere) Living with -0.029 -0.078 0.000 -0.153 ** -0.071 -0.022 0.072 (0.034) (0.049) (0.048) (0.077) (0.070) (0.063) (0.065) Leaving near -0.076 -0.140 -0.025 -0.028 -0.214 ** -0.194 0.104 (0.053) (0.115) (0.060) (0.186) (0.093) (0.152) (0.079) Nearest living daughter (reference: living elsewhere) Living with -0.007 -0.025 0.007 0.067 0.020 -0.073 0.001 (0.034) (0.049) (0.048) (0.075) (0.071) (0.064) (0.065) Leaving near 0.097 ** 0.033 0.149 *** 0.231 0.271 *** -0.075 0.070 (0.047) (0.112) (0.054) (0.198) (0.078) (0.140) (0.073) Second survey wave -0.137 *** -0.173 *** -0.084 ** -0.171 *** -0.052 -0.178 *** -0.110 ** (0.025) (0.036) (0.036) (0.055) (0.053) (0.048) (0.048) Constant 0.693 *** 0.432 * 0.234 0.074 0.585 0.454 -0.081 (0.147) (0.256) (0.259) (0.374) (0.395) (0.352) (0.334) No. of observations 10,295 5,961 4,334 2,553 1,998 3,408 2,336 Model tests (p-values) Model fit 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Random effects 0.000 0.000 0.000 0.000 0.000 0.000 0.000 *** = p<0.01, ** = p<0.05, and * = p<0.10. Source: Authors’ calculations using Malaysia Ageing and Retirement Survey data. 31 Table 2. Poisson Random Effects Estimates for Number of Chronic Health Conditions, Malaysia Variable/Statistic All Aged 40 – 59 Aged 60+ Men Aged 40 – 59 Men Aged 60+ Women Aged 40 – 59 Women Aged 60+ Woman 0.022 -0.029 0.081 ** (0.029) (0.047) (0.033) Age (years) 0.032 *** 0.056 *** 0.003 0.042 *** 0.008 ** 0.068 *** -0.002 (0.002) (0.005) (0.003) (0.007) (0.004) (0.006) (0.003) Ethnicity (reference: other Bumiputra) Malay 0.087 0.255 ** -0.069 0.081 -0.237 * 0.339 ** 0.071 (0.074) (0.108) (0.094) (0.173) (0.144) (0.142) (0.125) Chinese -0.036 -0.030 -0.082 0.051 -0.268 ** -0.158 0.071 (0.070) (0.105) (0.088) (0.165) (0.135) (0.138) (0.119) Indian 0.406 *** 0.722 *** 0.078 0.710 *** -0.037 0.693 *** 0.182 (0.087) (0.129) (0.107) (0.203) (0.168) (0.168) (0.141) Others -0.349 *** -0.598 *** -0.179 * -0.677 *** -0.321 ** -0.552 *** -0.092 (0.072) (0.107) (0.092) (0.168) (0.150) (0.137) (0.117) Education (reference: less than primary) Primary 0.077 * -0.053 0.042 -0.078 0.137 0.006 0.018 (0.044) (0.098) (0.046) (0.218) (0.100) (0.106) (0.052) Secondary -0.007 -0.216 ** 0.025 -0.037 0.195 * -0.265 ** -0.088 (0.048) (0.094) (0.051) (0.209) (0.101) (0.103) (0.062) Tertiary -0.095 -0.226 0.038 -0.043 0.269 ** -0.337 * -0.485 ** (0.082) (0.140) (0.096) (0.250) (0.126) (0.190) (0.227) Marital status (reference: married) Never married -0.040 -0.119 0.030 -0.262 -0.048 -0.051 0.052 (0.086) (0.126) (0.114) (0.195) (0.172) (0.182) (0.147) Widowed 0.103 *** 0.200 *** 0.119 *** 0.177 0.202 *** 0.149 * 0.085 * (0.036) (0.073) (0.038) (0.148) (0.069) (0.088) (0.046) Separated/ divorced 0.000 -0.013 0.078 0.138 -0.127 -0.104 0.134 (0.074) (0.099) (0.109) (0.167) (0.181) (0.129) (0.128) Urban -0.017 0.010 -0.035 0.007 -0.002 0.006 -0.061 ** (0.017) (0.029) (0.022) (0.051) (0.034) (0.035) (0.028) Peninsular Malaysia -0.031 -0.217 *** 0.141 *** -0.045 0.254 *** -0.338 *** 0.044 (0.041) (0.064) (0.048) (0.111) (0.072) (0.080) (0.068) Continued on the next page 32 Table 2 (continued) Variable/Statistic All Aged 40 – 59 Aged 60+ Men Aged 40 – 59 Men Aged 60+ Women Aged 40 – 59 Women Aged 60+ Living children (reference: none) 1–2 0.200 *** 0.045 0.247 ** -0.110 0.377 *** 0.162 0.155 (0.072) (0.105) (0.099) (0.171) (0.137) (0.140) (0.135) 3–4 0.209 *** 0.061 0.260 *** -0.109 0.323 ** 0.182 0.226 * (0.072) (0.109) (0.099) (0.181) (0.138) (0.143) (0.134) 5+ 0.203 *** -0.001 0.238 ** -0.051 0.265 * 0.054 0.228 * (0.077) (0.121) (0.102) (0.201) (0.145) (0.156) (0.137) Living arrangements (reference: live alone) Spouse only 0.014 -0.004 0.021 -0.138 0.040 0.091 -0.025 (0.055) (0.119) (0.060) (0.171) (0.083) (0.174) (0.083) Other family members -0.017 -0.115 0.063 -0.136 0.046 -0.082 0.053 (0.049) (0.107) (0.053) (0.138) (0.074) (0.165) (0.070) Nearest living son (reference: living elsewhere) Living with -0.054 * 0.071 -0.118 *** 0.086 -0.160 *** 0.084 -0.077 (0.031) (0.053) (0.037) (0.089) (0.056) (0.067) (0.048) Leaving near 0.032 0.006 0.051 0.293 * 0.068 -0.169 0.026 (0.042) (0.116) (0.044) (0.169) (0.068) (0.157) (0.057) Nearest living daughter (reference: living elsewhere) Living with -0.022 -0.009 0.032 0.028 0.065 -0.023 0.017 (0.031) (0.050) (0.037) (0.081) (0.057) (0.065) (0.049) Living near 0.084 ** 0.343 *** 0.072 * 0.583 *** 0.105 0.180 * 0.051 (0.037) (0.083) (0.041) (0.145) (0.065) (0.103) (0.051) Second survey wave 0.247 *** 0.242 *** 0.244 *** 0.287 *** 0.240 *** 0.219 *** 0.245 *** (0.018) (0.029) (0.022) (0.052) (0.036) (0.035) (0.029) Constant -2.167 *** -3.121 *** -0.343 -2.513 *** -0.854 *** -3.735 *** 0.135 (0.148) (0.317) (0.225) (0.530) (0.329) (0.414) (0.297) No. of observations 10,339 5,976 4,363 2,561 2,019 3,415 2,344 Model tests (p-values) Model fit 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Random effects 0.000 0.000 0.000 0.000 0.000 0.000 0.000 *** = p<0.01, ** = p<0.05, and * = p<0.10. Source: Authors’ calculations using Malaysia Ageing and Retirement Survey data. 33 Table 3. Poisson Random Effects Estimates for Number of Depressive Symptoms, Viet Nam Variable/Statistic All Age d 60+ All Me n Men Age d 60+ All Women Women Age d 60+ Woman 0.199 *** 0.154 *** (0.000) (0.000) Age (years) 0.007 *** 0.006 *** 0.014 *** 0.009 *** 0.003 *** 0.004 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Kinh ethnicity -0.031 *** -0.014 *** 0.016 *** -0.032 *** -0.041 *** -0.015 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Education (reference: less than primary) Primary -0.074 *** -0.042 *** -0.032 *** -0.018 *** -0.110 *** -0.075 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Secondary -0.262 *** -0.194 *** -0.219 *** -0.205 *** -0.294 *** -0.200 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Tertiary -0.531 *** -0.488 *** -0.531 *** -0.467 *** -0.518 *** -0.486 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Marital status (reference: married) Never married -0.249 *** -0.215 *** 0.242 *** 0.335 *** -0.296 *** -0.279 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Widowed 0.054 *** 0.055 *** -0.076 *** -0.047 *** 0.125 *** 0.103 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Separated/divorced -0.022 *** 0.198 *** 0.171 *** 0.026 *** -0.064 *** 0.177 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Urban -0.135 *** -0.137 *** -0.053 *** -0.068 *** -0.209 *** -0.207 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Region (reference: northern) Central 0.047 *** 0.029 *** 0.048 *** 0.014 *** 0.037 *** 0.036 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Southern -0.031 *** -0.005 *** 0.029 *** 0.064 *** -0.080 *** -0.054 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Living children (reference: none) 1–2 -0.261 *** -0.174 *** -0.486 *** -0.450 *** -0.124 *** -0.048 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 3–4 -0.199 *** -0.154 *** -0.236 *** -0.127 *** -0.163 *** -0.159 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 5+ -0.142 *** -0.120 *** -0.165 *** -0.052 *** -0.138 *** -0.174 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Continued on the next page 34 Table 3 (continued) Variable/Statistic All Aged 60+ All Men Men Aged 60+ All Women Women Aged 60+ Living arrangements (reference: live alone) Spouse only -0.230 *** -0.179 *** -0.402 *** -0.349 *** -0.091 *** -0.072 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Other family members -0.136 *** -0.078 *** -0.369 *** -0.301 *** -0.021 *** 0.017 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Nearest living child (reference: living elsewhere) Living with -0.101 *** -0.091 *** 0.075 *** 0.019 *** -0.205 *** -0.180 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Leaving near -0.042 *** -0.039 *** 0.024 *** 0.001 *** -0.104 *** -0.092 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 2022 survey wave -0.098 *** -0.121 *** -0.180 *** -0.210 *** -0.037 *** -0.052 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Constant 1.317 *** 1.288 *** 0.897 *** 1.206 *** 1.777 *** 1.591 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) No. of observations 7,192 5,941 2,935 2,423 4,257 3,518 Model tests (p-values) Model fit 0.000 0.000 0.000 0.000 0.000 0.000 Random effects 0.000 0.000 0.000 0.000 0.000 0.000 *** = p<0.01, ** = p<0.05, and * = p<0.10. Source: Authors’ calculations using Viet Nam Aging Survey data. 35 Table 4. Poisson Random Effects Estimates for Number of Chronic Health Conditions, Viet Nam Variable/Statistic All Age d 60+ All Men Men Age d 60+ All Women Women Age d 60+ Woman 0.104 *** 0.109 *** (0.000) (0.000) Age (years) 0.009 *** 0.005 *** 0.014 *** 0.013 *** 0.005 *** 0.000 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Kinh ethnicity 0.129 *** 0.068 *** 0.050 *** 0.052 *** 0.158 *** 0.077 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Education (reference: less than primary) Primary -0.039 *** -0.030 *** -0.142 *** -0.176 *** 0.032 *** 0.074 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Secondary 0.132 *** 0.147 *** 0.159 *** 0.164 *** 0.097 *** 0.104 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Tertiary 0.141 *** 0.127 *** 0.230 *** 0.201 *** 0.013 *** -0.016 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Marital status (reference: married) Never married -0.109 *** -0.174 *** 0.088 *** -0.096 *** -0.174 *** -0.171 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Widowed -0.009 *** 0.018 *** -0.126 *** -0.100 *** 0.039 *** 0.070 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Separated/divorced -0.087 *** 0.030 *** 0.066 *** -0.143 *** -0.114 *** 0.043 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Urban 0.032 *** 0.062 *** 0.117 *** 0.144 *** -0.036 *** -0.002 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Region (reference: northern) Central -0.057 *** -0.053 *** -0.052 *** -0.055 *** -0.062 *** -0.045 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Southern 0.140 *** 0.124 *** 0.078 *** 0.051 *** 0.186 *** 0.176 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Living children (reference: none) 1–2 0.090 *** 0.040 *** 0.535 *** 0.090 *** -0.042 *** 0.050 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 3–4 0.125 *** 0.010 *** 0.605 *** 0.137 *** -0.047 *** -0.037 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 5+ 0.305 *** 0.229 *** 0.781 *** 0.360 *** 0.122 *** 0.161 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Continued on the next page 36 Table 4 (continued) Variable/Statistic All Aged 60+ All Men Men Aged 60+ All Women Women Aged 60+ Living arrangements (reference: live alone) Spouse only -0.134 *** -0.047 *** -0.267 *** -0.175 *** -0.050 *** 0.029 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Other family members -0.094 *** -0.023 *** -0.146 *** -0.051 *** -0.066 *** -0.018 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Nearest living child (reference: living elsewhere) Living with -0.097 *** -0.117 *** -0.169 *** -0.194 *** -0.042 *** -0.069 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Leaving near -0.035 *** -0.064 *** -0.072 *** -0.097 *** 0.011 *** -0.032 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 2022 survey wave 0.489 *** 0.466 *** 0.434 *** 0.441 *** 0.521 *** 0.477 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Constant -0.456 *** -0.117 *** -1.054 *** -0.606 *** -0.051 *** 0.346 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) No. of observations 7,498 6,239 3,035 2,519 4,463 3,720 Model tests (p-values) Model fit 0.000 0.000 0.000 0.000 0.000 0.000 Random effects 0.000 0.000 0.000 0.000 0.000 0.000 Notes: The notation *** is p<0.01, ** is p<0.05, and * is p<0.10. Source: Authors’ calculations using Viet Nam Aging Survey data. ASIAN DEVELOPMENT BANK ASIAN DEVELOPMENT BANK 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org ADB ECONOMICS WORKING PAPER SERIES NO. 730 June 2024 The Role of Family Support in the Well-Being of Older People Evidence from Malaysia and Viet Nam Rapid demographic changes in Malaysia and Viet Nam could disrupt traditional family support for older people. An analysis of unique panel data from the Malaysia Ageing and Retirement Survey and the Viet Nam Aging Survey points to the benefits of living conditions—including marital status and whether one’s children live nearby—for the physical and mental well-being of older people. Given the estimated protective effect of living arrangements examined in the paper, governments may need to adjust social safety nets to bolster the physical and mental health of senior citizens living alone. About the Asian Development Bank ADB is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 68 members —49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. THE ROLE OF FAMILY SUPPORT IN THE WELL-BEING OF OLDER PEOPLE EVIDENCE FROM MALAYSIA AND VIET NAM Yana van der Meulen Rodgers, Joseph E. Zveglich, Jr., Khadija Ali, and Hanna Xue