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10

Household Dynamics and Living Arrangements of the Elderly in Indonesia: Evidence from a Longitudinal Survey1

Firman Witoelar

Like many other developing countries in Asia, Indonesia is experiencing rapid population aging (Kinsella and He, 2009). The average number of children born per women has declined from around 4 in the early 1980s to around 2.5 in 2000, while life expectancy has increased from around 56 to 68 during the same period. In 2005, the percentage of those aged 60 and older was around 7.5% of the total population. While this is a lower percentage than, for instance, Singapore or even Thailand, it still amounts to 16 million people, given Indonesia’s population size (Ananta and Arifin, 2009). One of the consequences of these demographic changes over the past few decades is that families are smaller and the number of children from whom parents can draw support at a later age also becomes smaller. This is particularly important in Indonesia and in other developing countries in the region, where social programs and pension schemes to support the elderly are lacking.2 In Indonesia, as

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1 I gratefully acknowledge the financial support of the World Bank’s Research Support Budget (RF-P121879-RESE-BBRSB). Earlier results of the paper were presented at the Conference on Policy Research and Data Needs to Meet the Challenges and Opportunities of Population Aging in Asia, New Delhi, March 14-15, 2011. All errors are mine. These are the views of the author and should not be attributed to the World Bank and its member countries.

2 See Abikusno (2009) for a discussion on past and recent laws and government policies related to older persons in Indonesia. Although 1966-1998 saw few policies that addressed aging issues, the recognition of the issues and waves of reforms in 1998 brought about laws and policies that are seen to be more favorable to older persons (including one on pension). In 2004, a law on the comprehensive national security system was passed that contains articles written to protect the pension sector.



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10 Household Dynamics and Living Arrangements of the Elderly in Indonesia: Evidence from a Longitudinal Survey1 Firman Witoelar L ike many other developing countries in Asia, Indonesia is experi- encing rapid population aging (Kinsella and He, 2009). The average number of children born per women has declined from around 4 in the early 1980s to around 2.5 in 2000, while life expectancy has increased from around 56 to 68 during the same period. In 2005, the percentage of those aged 60 and older was around 7.5% of the total population. While this is a lower percentage than, for instance, Singapore or even Thailand, it still amounts to 16 million people, given Indonesia’s population size (Ananta and Arifin, 2009). One of the consequences of these demographic changes over the past few decades is that families are smaller and the number of children from whom parents can draw support at a later age also becomes smaller. This is particularly important in Indonesia and in other developing countries in the region, where social programs and pension schemes to support the elderly are lacking.2 In Indonesia, as 1 I gratefully acknowledge the financial support of the World Bank’s Research Support Budget (RF-P121879-RESE-BBRSB). Earlier results of the paper were presented at the Confer- ence on Policy Research and Data Needs to Meet the Challenges and Opportunities of Popu - lation Aging in Asia, New Delhi, March 14-15, 2011. All errors are mine. These are the views of the author and should not be attributed to the World Bank and its member countries. 2 See Abikusno (2009) for a discussion on past and recent laws and government policies related to older persons in Indonesia. Although 1966–1998 saw few policies that addressed aging issues, the recognition of the issues and waves of reforms in 1998 brought about laws and policies that are seen to be more favorable to older persons (including one on pension). In 2004, a law on the comprehensive national security system was passed that contains articles written to protect the pension sector. 229

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230 AGING IN ASIA in other developing countries in the region, support for the elderly has primarily come from networks of families or relatives, with remittances from children living elsewhere and shared residence being the two most important mechanisms. In addition to demographic pressure, there have also been concerns that pressure from “modernization” would weaken traditional family structures. Moreover, as the population ages, Indonesia is experienc- ing nutrition and health transitions with the population moving out of undernutrition and communicable diseases and the elderly popula - tion increasingly exposed to higher risk factors correlated with chronic health problems (Witoelar, Strauss, and Sikoki, 2009). In Indonesia, early concerns about the implications of population aging, rapid economic changes, and changing health challenges on traditional familial support systems and family structures have been brought up by Hugo (1992) and by Wirakartakusumah et al. (1997). Despite these concerns, a number of empirical studies on aging and living arrangements in Southeast Asia done in the late 1990s suggest that shared living remains common and the decline in co-residency was modest, as was reviewed by Frankenberg, Chan, and Ofstedal (2002) and by Beard and Kunharibowo (2001). One of the aims of this chapter is to revisit the question and see how much the pattern of living arrangements among the elderly has changed. Data from the National Socioeconomic Survey (the Susenas), the nationally representative survey of households conducted annually in Indonesia, indeed show that the living arrangements among the elderly had not changed considerably between 1993 and 2007. Figures 10-1 and 10-2 show living arrangements by age in 1993 and 2007 for men and women aged 55 and older, respectively. The figures show that, like in many countries in Asia, most older adults in Indonesia co-reside with at least one of their children. There are differences in the living arrangement patterns between males and females, as will be discussed later in this chapter. Over- all, the patterns do not seem to have changed over the years. (Similar pat- terns emerge when we use data from other years of the Susenas between 1993 and 2007.) These figures seem to still be consistent with what some previous studies have found on the patterns of living arrangements of the elderly in Southeast Asia. Frankenberg, Chan, and Ofstedal (2002) found that in Indonesia, Singapore, and Taiwan, the pattern of living arrange - ments is relatively stable, at least throughout the 1990s. It is important to note, however, that the Susenas is not particu- larly well suited for analysis of this kind. First, one could only identify relationships in the household relative to the household head. Second, only limited socioeconomic characteristics were collected. Third and per- haps most importantly, the survey does not collect any information on nonco-resident family members. In addition, cross-sectional analysis may

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Male 55+, 1993 Male 55+, 2007 .8 .8 .6 .6 .4 .4 Proportion Proportion .2 .2 0 0 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 Age Age Alone With Spouse Alone With Spouse With Child Other With Child Other FIGURE 10-1 Living arrangements of males aged 55 and older. 231 SOURCE: Data from National Socioeconomic Survey (Susenas), 1993 and 2007.

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Female 55+, 1993 Female 55+, 2007 232 .8 .8 .6 .6 .4 .4 Proportion Proportion .2 .2 0 0 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 Age Age Alone With Spouse Alone With Spouse With Child Other With Child Other FIGURE 10-2 Living arrangements of females aged 55 and older. SOURCE: Data from National Socioeconomic Survey (Susenas), 1993 and 2007.

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233 FIRMAN WITOELAR mask what has really been happening to elderly living arrangements over the 14-year period. For example, studies have shown that in the wake of the Asian financial crisis in 1997–1999, one of the mechanisms used to cope with the crisis was to combine households (Frankenberg, Smith, and Thomas, 2003). Such episodes highlight the reality that co- residency and parental home-leaving are not merely lifecycle events. The Indonesia Family Life Survey (IFLS), a longitudinal household survey that spans 1993 to 2007, does not have these limitations. The four rounds of the survey follow individuals over 14 years. Since the first round of the survey, household rosters listing all household mem- bers were completed. In addition to documenting the relationship of household members to the head, the roster also contains information that enables the researcher to link children to their biological parents. Rich information about households and individuals were collected, including socioeconomic variables, such as education, consumption, income, and labor market outcomes, as well as health. In addition, the survey also collects information of nonco-resident family members.3 This chapter is descriptive in nature, and its main objectives are straightforward. First, I want to document the pattern of living arrange - ments of the population aged 55 and older, by gender, over the sur- vey years and see whether there have been significant changes over the 14 years. Second, I want to look at key socioeconomic characteristics that we hypothesize to be correlated with living arrangements. I first look at cross-section correlations between the covariates and living arrangements in the base year 1993 for those aged 55 and older. I also want to know whether relationships that existed in cross-sectional analysis hold in the longitudinal analysis. I use these baseline characteristics and study their correlations with living arrangements 14 years later. The chapter is organized as follows. The next section will discuss the data and the key socioeconomic variables that we use in the analysis. I then document the living arrangements of individuals aged 55 and older in each of the survey years—1993, 1997, 2000, and 2007—as if they were independent cross-sections. Next, I employ a multivariate framework to look at cross-sectional correlations in the base year of 1993. Finally, I use the longitudinal sample of individuals to look at correlations between those key variables at the baseline year (1993) and living arrangements 14 years later under both the linear probability models (LPM) and multi - nomial logit (MNL) models. 3 See Strauss et al. (2009a) for the overview of the IFLS Wave 4.

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234 AGING IN ASIA DATA AND METHODOLOGY Data The Indonesia Family Life Survey is a large-scale, broad-based longi- tudinal survey of households, individuals, and communities with detailed questions on a vast number of socioeconomic characteristics of the respon- dents. As noted above, the survey has been fielded four times (1993, 1997, 2000, 2007), covering a span of 14 years. The survey collects detailed questions about household membership, including questions about non- coresident family members, which is crucial for studies that aim to look at issues related to changing household structure and living arrangements. While the IFLS was not originally designed to specifically study aging and its consequences, it was expanded in the last round (IFLS4 2007) to include questions related to aging. The questions added were specifically chosen to be comparable to questions being asked in surveys on aging around the world, such as the Health and Retirement Study (HRS) in the United States; Survey of Health, Ageing and Retirement in Europe (SHARE), Korean Longitudinal Study of Ageing in South Korea (KLoSA), China Health and Retirement Longitudinal Study (CHARLS) in China, and the new Longitudinal Aging Study in India (LASI). IFLS4 has the advantage of having detailed information of the now “elderly” respondents when they were younger. In this chapter, when the focus is on cross-sectional relationships between living arrangements of the elderly and household as well as individual covariates, I will restrict the sample on those aged 55 and older during the time of the survey (1993, 1997, 2000, and 2007), and the attention will be restricted to those who have at least one living child. In the longitudinal part of the analysis, where the focus is on the relation- ships between living arrangements in 2007 with covariates in the baseline year, 1993, we will restrict the sample to those who were 55 and older in 2007. The covariates come from 1993, when the individuals were 41 and older. One reason to look at the sample of those who were 55 and older in 2007, rather than focusing on those who were 55 and older in 1993 and see what happened 14 years later, is that I will not have to worry as much about mortality selection. Also, defining the sample in this way allows us to work with a significantly larger sample (around 3,800 individuals) as opposed to using the sample consisting of individuals 55 years and older who were also interviewed in 1993. As in any longitudinal household survey, attrition becomes a con - cern, especially for a survey that spans over long period of time. IFLS has maintained a relatively low attrition rate, with around 90% of IFLS1 households and around 80% of IFLS1 household members re-contacted in

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235 FIRMAN WITOELAR IFLS4. The low attrition rate was not due to low mobility; in fact, almost one-third of those interviewed in 1993 had moved by 2007. However, the survey managed to lower the attrition rate by tracking down some of the movers (see Thomas et al., forthcoming). For the older age group, the main cause of attrition is death. Of the household members aged 40 to 80 in 1993 (the main sample in this paper), the re-contact rate in 2007 was 95% with the following breakdown: around 70% were found, 25% had died, and 5% were not found (see Table 2.5 in Strauss et al., 2009b). To address this concern, I employ attrition-corrected person-weights when I look at living arrangement patterns in Tables 10-1 and 10-2. Methodology As the framework for this analysis, I considered four types of mutu- ally exclusive and exhaustive living arrangements: (1) elderly living alone, where the household does not contain anyone but elderly; (2) elderly liv - ing with a spouse, where the household consists of only the elderly person and the spouse (who may or may not be elderly); (3) elderly living with at least one adult child, when the household contains at least one adult child of the elderly;4 and (4) other form of living arrangement, a residual category that includes households where the elderly live with siblings’ family, with immediate family of his/her children but not with any of one his/her children, and so forth. The analysis focuses on adult children since one of main reasons one cares about co-residence is to look at elderly support. Only biological chil- dren are considered as children in the analysis. Therefore, an older adult who lives only with his/her daughter-in-law will not be categorized as living with a child, but will be indicated as living in the “other” category. An older adult who lives with a servant will be counted as living in the “other” category rather than “living alone.” Multivariate Analysis Using the sample of those aged 55 and older in 1993, I first look at cross-sectional correlations between individual characteristics and living arrangement using a simple linear probability model (LPM) with the dependent variable equal to 1 if the individual co-resided with at least one biological child in 1993. Still using the LPM and the 1993 covariates, 4 Adult child here is defined as aged 15 and older. Note that as long as an adult child lives in the household, the elderly will be included in this category, including those who live with or without a spouse or with other people.

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236 AGING IN ASIA we then look at the probability of co-residence in 2007 for individuals who were aged 55 and older in 2007. I follow the literature in this area by adopting a multinomial logit model of living arrangements. While there exist a large number of studies in the literature using this approach with cross-sectional data, the use of detailed panel data in this kind of analysis has so far been limited, especially for developing countri es, due to the availability of the data. I set those living alone as the base group and then examine the relative risks of living only with a spouse, co-residing with a child, or living in another arrangement, as well as the marginal effects of changing one of the covariates. The usual assumption of the multinomial logit, the inde - pendence from irrelevant alternatives (IIA), applies. It implies that the relative probabilities for any of two available alternatives depend only on the attributes of those alternatives. In particular, it assumes that the unobservables in each alternative are not correlated with each other. Covariates In the multivariate analyses, I first put a focus on a limited number of variables at the baseline that are likely to have already been determined during the time of the survey. I use the individual’s own age, education, and the total number of surviving sons and daughters in the basic speci - fication. For age, we use dummy variables indicating whether the indi- viduals are aged 60–64, or 65 and older, with the group aged 55–59 being the omitted category. The non-linearity of the relationship between own age and living arrangements is apparent from the figures, which we want to capture in the multivariate context. I created dummy variables indicating whether individuals have some primary education, completed primary education, or completed junior high school, using the group of those without schooling as the base cat - egory. The total number of living children at the time of the survey pro- vides us with the potential number of sources of support for the elderly. I expect this variable to be positively correlated with co-residency. Here, it is crucial that I include not only children who are listed in the household rosters, but also other children living elsewhere.5 I use separate variables to indicate sons and daughters since anthro - pological literature on Indonesia suggests that gender is an important factor determining who will take care of the parents in old age, and it varies between ethnicities in Indonesia. I include a variable indicating 5 Because of this requirement, I only include individuals aged 55 or older who were indi - vidually interviewed in 1993 since we only have information for these individuals regarding nonco-resident family members.

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237 FIRMAN WITOELAR the age of the oldest child. In results not shown, I also include per capita expenditure—a proxy of income—as one of the covariates.6 Well-known studies from developed countries, such as the study by Costa (1997), have shown that income plays an important role enabling elderly to live alone. Privacy of both parents and children as a normal good has been modeled in studies of living arrangements in developed countries (see, for example, Ermisch, 1999). In the current study, the results show that per capita expenditure (pce) did not have statistically significant relationships with living arrangements of the elderly. I then add information about the marital status of the elderly, and for those who are married, age of the spouse and the spouse’s education. In some specifications, I also consider several variables that we usually do not want to include as explanatory variables in cross-sectional analysis of living arrangements, such as information about labor participation of the individuals, their spouses, and their co-resident children. Employment decisions may very well be determined jointly with living arrange- ment decisions, although in Indonesia, Cameron and Cobb-Clark (2002) find little evidence that old-age support from children through financial transfer and co-residence affects the labor supply decisions of the elderly. Finally, I use two variables measuring (subjectively) the health condi - tions of the individuals at the baseline year. First, I use self-assessment of basic physical functioning and Activities of Daily Living (ADLs). ADLs provide useful information about a person’s functional status and have been shown to be correlated with socioeconomic status (SES) measures (see, for instance, National Socioeconomic Survey, 1993, 2007). The second measure I use is General Health Status (GHS). In all four waves of IFLS, respondents were asked the question, “In general, how is your health?” with the following options: very healthy, somewhat healthy, somewhat unhealthy, and unhealthy. Those who answered somewhat unhealthy and unhealthy were coded to have poor health. LIVING ARRANGEMENT PATTERNS This section begins by going back to Figures 10-1 and 10-2 that show the patterns of living arrangement of adults aged 55 and older using the Susenas data from 1993 and 2007.7 The pattern that emerges from the fig- 6 Per capita consumption is constructed from the household consumption expenditure module of IFLS, which reports the market expenditures as well as own production of house - holds on food and nonfood items including on durable and nondurable goods. 7 Note that the Susenas does not separate biological and nonbiological children in catego - rizing relationship to the head of the households. The Susenas also does not separate par- ent and parent-in-law of the head of the households. Numbers used to create Figures 10-1 and 10-2 thus combine biological with non-biological children, parent with parent-in-law.

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238 AGING IN ASIA ures also shows, in contrast to women, as men age, they seem to rely less on their children but more on their spouses. For women, the likelihood of sharing residence with a child decreases with age before it increases again. At all ages, the percentage of men co-residing with a spouse is always higher than the percentage of those living alone or in other living arrange- ments. Figures created using other years of the Susenas between 1993 and 2007 (not shown) have a similar pattern. As mentioned, other than being available for every year, the Susenas is not well suited for this analysis. Figures 10-3 and 10-4 use data from the IFLS1 and IFLS4 to look at living arrangements of IFLS respondents who are aged 55 and older.8 The majority of elderly men live with their adult child, and the age patterns do not seem to change between 1993 and 2007. The figures show, as in the Susenas data, a declining line describing the proportion of men aged 55 and older who live with at least a child by age, respectively. As they age, men are less likely to live with their adult children and more likely to live only with their spouses. From the figures alone, there does not seem to be a movement into co-residence as men age. The proportion living alone increases as men age, but it is well below 2% even for the oldest of the elderly. The patterns for women show that in 1993, as in the Susenas, the pro - portion of elderly women living with a child decreases with age before increasing sharply at older age. For 2007, however, the U-shaped line is much less apparent. At the same time, the proportion of elderly women living alone increases with age. The U-shaped pattern is particularly inter- esting since the upturn suggests that as elderly women age they tend to move into shared living arrangements with their adult children, either by moving into the children’s households or by taking in the children who have left their households earlier. This pattern is consistent with the pat - tern of old-age support of elderly women by the children. Both for men and women, “other” living arrangements do not change with age. Table 10-1 presents the distributions of living arrangements in each of the four waves of IFLS of individuals aged 55 and older. The table treats each wave of IFLS as if it were an independent, cross-sectional sample. The sample is weighted using cross-section person-weight that accounts for attrition.9 The table shows that living arrangement patterns seem to 8 For the figures from IFLS to be comparable to the Susenas figures, biological and non- biological children are both included in the calculation. Parent and parent-in-law are also combined. In the analysis, however, only biological child and parent are used when we define parent-child shared living arrangement. 9 The estimates using the cross-sectional person-weights will be representative of the Indo - nesian population living in the IFLS provinces in 1993 (for IFLS1), 1997 (IFLS2), 2000 (IFLS3), and 2007 (IFLS4). See Strauss et al. (2009b) for a discussion of how the person-weights are constructed.

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Male 55+, 2007 Male 55+, 1993 1 1 .8 .8 .6 .6 Proportion Proportion .4 .4 .2 .2 0 0 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 Age Age Alone With Spouse Alone With Spouse With Child Other With Child Other FIGURE 10-3 Living arrangements of males aged 55 and older. 239 SOURCE: Data from Indonesia Family Life Survey, IFLS 1 and IFLS4.

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250 AGING IN ASIA daughters, on the other hand, is positively correlated with co-residency in all specifications. Elderly women who co-reside with their children are significantly more likely to co-reside with a daughter than with a son. Finally, neither the ADL nor the GHS in 1993 seems to be correlated with the probability of elderly men co-residing 14 years later. For women, however, poor GHS in 1993 has a positive correlation with the probability to co-reside with their children in 2007. The multinomial logit results add some insights. For each male and female, results from two specifications were presented. The first speci- fication includes only the basic specification without the characteristics of the spouse. The second specification includes spouse’s characteris- tics and variables indicating work status of the respondent, the spouse, and the child, as well as health status of the elderly. Tables 10-8 and 10-9 show how predicted probabilities change with a change in a covariate, holding other variables at their means. These tables are based on regres- sion results presented in Appendix Tables 10-A1 and 10-A2.10 Table 10-8 shows, for example, that an additional son would increase the probability of co-residence by 0.061, while an additional daughter would increase it by 0.042. Note, however, that from Appendix Table 10-A1, most of the coefficients for males are not statistically significant. For women, similar to the LPM results, the likelihood of co-residency with a child is higher, the more educated the children are. Table 10-8 shows that an increase of 1 year of maximum education of the children at the baseline year increases the probability of living with a child 14 years later by 0.010. For men, the coefficient is not significant and the marginal effect is negative. For women, having a spouse who works in the baseline year increases the likelihood of living with the spouse and decreases the likelihood of living with an adult child (see Appendix Table 10-A2). Again, this is con - sistent with the possibility that an elderly couple may value privacy and will tend to choose not to co-reside with a child if they have adequate resources. The results on age are consistent with what we have seen from the LPM results, and the U-shaped relationship between age and the likelihood of co-residency of elderly women is not evident. In fact, both older age groups show lower odds for co-residency compared to the age group 55–59 for both men and women. ) = Pr ( y= j x ∂Pr ( y = j x ) {β − ∑ Pr ( y = j x )} j 10 The marginal effects is defined as where j jk ∂ xk h=1 represents the possible outcomes (j = 0,1,2,3) and x is the vector of covariates. The discrete change ( ) = Pr ∆ Pr y = j x ( y= j x , x ) ( ) = 1 − Pr y = j x , xk = 0 . (for example from xk = 0 to xk = 1) is defined as k ∆xk

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251 FIRMAN WITOELAR CONCLUSIONS In the 14 years between 1993 and 2007, there does not seem to be much change in living arrangements. The first thing to note from the descriptive results is that by focusing on age 55 and above for both men and women, we may be focusing on different lifecycle stages of the individuals. Because men tend to marry later and marry younger women, the sample of men consists of a much larger fraction of married men compared to women in the same age group. Mortality selection may play a role in the differences across groups, too, where relatively healthier men are observed in the sample. Own education and whether or not the elderly or his/her spouse is working in the base year are negatively correlated with the probability of living with an adult child, suggesting that elderly with more human capital (and household resources) may prefer living by themselves to liv- ing in a shared residence with their children. There are gender differences in how own, spouse, or child’s charac - teristics at the baseline correlate with living arrangements. In particular, children’s potential earning (as measured by years of education) and work status seem to increase likelihood of co-residency for women, but not as much for men. Further examination may tell us how much lifecycle variables such as age play a role in influencing a transition between living arrangements. More work should be done in looking at the transition, in particular since the four waves of IFLS would permit looking at transition between four different points in time (for respondents who were interviewed in all waves). More insights could be gained by such an exercise. While the pattern of living arrangements seems to stay constant for now, demographic pressure will likely affect living arrangements as the population ages further. One important caveat of this paper is that the analysis excludes elderly who have no surviving children at the time of the survey—around 6 to 8%. This selected sample could include those who would be most vulnerable in old age.

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252 AGING IN ASIA TABLE 10-8 Changes in Predicted Probabilities from MNL: Living Arrangements of Males, Aged 55+, in 2007 Pr (y|x) x sd(x) Marginal Effect Number of sons 2.151 1.499 ± 1 standard deviation Marginal effect Number of daughters 2.149 1.434 ± 1 standard deviation Marginal effect Max. years of children’s educ. 6.060 5.073 ± 1 standard deviation Marginal effect Discrete Change from 0 to 1 Own age Aged 60−69 0.418 0.493 Age 70 0.275 0.447 Own education Completed primary 0.313 0.464 Completed junior high 0.267 0.442 Completed senior high 0.238 0.426 Maximum age of child > 15 0.945 0.228 Divorced/widowed/never married 0.017 0.131 Spouse’s age Aged 60−69 0.043 0.202 Age 70 0.003 0.058 Spouse’s education Completed primary 0.334 0.472 Completed junior high 0.206 0.405 Completed senior high 0.136 0.343 0.576 0.494 Rural labor participation Working 0.912 0.283 Spouse working 0.467 0.499 Any child working 0.325 0.469 Health status Any problem with ADLs 0.106 0.308 “Poor” GHS 0.107 0.309 NOTE: This table is based on regressions in Appendix Table 10-A1, Specification 2. SOURCE: Author’s calculation based on data from Indonesia Family Life Survey, IFLS1 and IFLS4.

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253 FIRMAN WITOELAR With Adult Other Living Living Alone With Spouse Child Arr. 0.209 0.659 0.124 0.008 Ave. With With Adult Other Living Living Alone |Change| Spouse Child Arr. (base) 0.023 –0.018 0.043 –0.027 0.002 0.031 –0.034 0.061 –0.026 –0.001 0.023 –0.015 0.046 –0.005 –0.026 0.021 –0.034 0.042 –0.009 0.000 0.011 0.022 –0.006 –0.014 –0.014 0.002 0.004 –0.001 –0.003 0.000 0.039 0.057 –0.078 0.012 0.010 0.087 0.131 –0.174 0.029 0.014 0.032 –0.060 0.050 0.015 –0.005 0.044 –0.076 0.088 –0.008 –0.003 0.090 –0.135 0.179 –0.038 –0.007 0.042 0.085 –0.037 –0.045 –0.003 0.088 –0.176 0.125 0.011 0.039 0.017 0.010 –0.032 0.023 –0.002 0.346 0.059 –0.692 0.569 0.064 0.016 0.000 –0.033 0.030 0.002 0.005 –0.003 0.009 –0.006 –0.001 0.025 0.018 –0.046 0.031 –0.003 0.030 0.048 –0.053 0.011 –0.006 0.018 –0.018 –0.018 0.035 0.001 0.034 0.067 –0.067 0.000 0.001 0.032 –0.051 0.065 –0.013 –0.001 0.005 0.010 –0.005 –0.003 –0.002 0.015 –0.030 0.020 0.008 0.002

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254 AGING IN ASIA TABLE 10-9 Changes in Predicted Probabilities from MNL: Living Arrangements of Females, Aged 55+, in 2007 Pr (y|x) x sd(x) Marginal Effect Number of sons 2.262 1.565 ± 1 standard deviation Marginal effect Number of daughters 2.189 1.544 ±1 standard deviation Marginal effect Max. years of children’s educ. 6.513 5.085 ± 1 standard deviation Marginal effect Discrete Change from 0 to 1 Own age Aged 60–69 0.448 0.497 Age 70 0.269 0.444 Own education Completed primary 0.266 0.442 Completed junior high 0.170 0.375 Completed senior high 0.117 0.321 Maximum age of child > 15 0.988 0.111 Divorced/widowed/never married 0.221 0.415 Spouse’s age Aged 60–69 0.197 0.398 Age 70 0.057 0.231 Spouse’s education Completed primary 0.231 0.421 Completed junior high 0.191 0.393 Completed senior high 0.154 0.361 0.577 0.494 Rural labor participation Working 0.516 0.500 Spouse working 0.641 0.480 Any child working 0.426 0.495 Health status Any problem with ADLs 0.275 0.447 “Poor” GHS 0.140 0.347 NOTE: This table is based on regressions in Appendix Table 10-A2, Specification 2. SOURCE: Author’s calculation based on data from Indonesia Family Life Survey, IFLS1 and IFLS4.

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255 FIRMAN WITOELAR With Adult Other Living Living Alone With Spouse Child Arr. 0.209 0.659 0.124 0.008 Ave. With With Adult Other Living Living Alone |Change| Spouse Child Arr. (base) 0.014 –0.011 0.028 –0.017 0.001 0.023 –0.018 0.043 –0.027 0.002 0.023 –0.015 0.046 –0.005 –0.026 0.015 –0.010 0.030 –0.004 –0.017 0.026 –0.006 0.052 –0.021 –0.025 0.005 –0.001 0.010 –0.004 –0.005 0.046 0.028 –0.092 0.035 0.029 0.080 0.039 –0.160 0.091 0.030 0.011 0.001 –0.011 0.022 –0.012 0.011 0.007 –0.021 0.000 0.015 0.016 0.016 0.017 –0.022 –0.011 0.059 0.006 –0.117 0.051 0.061 0.091 –0.167 0.149 –0.015 0.032 0.038 –0.014 –0.039 –0.023 0.075 0.066 –0.045 –0.050 –0.036 0.131 0.010 –0.004 0.020 –0.002 –0.014 0.027 0.003 0.050 –0.009 –0.044 0.021 –0.024 0.041 –0.005 –0.013 0.013 0.015 –0.015 0.010 –0.010 0.025 0.023 –0.017 –0.034 0.028 0.031 0.039 0.010 0.013 –0.062 0.053 –0.030 0.107 –0.036 –0.041 0.008 0.005 –0.016 0.011 0.000 0.028 –0.013 –0.042 0.014 0.041

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256 AGING IN ASIA APPENDIX TABLE 10-A1 MNL of Living Arrangements, 1993–2007, Males,Aged 55+, in 2007 (living alone is the base outcome) Specification 1 with an with spouse adult child other RRR Z RRR Z RRR Z Aged 60−69 0.467 –1.52 0.323* –2.33 0.384 –1.88 Age 70+ 0.540 –1.16 0.236** –2.79 0.381 –1.78 Some primary school 1.584 1.04 2.220 1.84 2.311 1.81 Completed primary 1.519 0.90 2.310 1.85 1.781 1.18 Completed junior high 2.330 1.44 5.384** 2.96 3.081 1.84 # of living sons 1.022 0.19 1.341 2.58 0.955 –0.37 # of living daughters 0.861 –1.30 1.085 0.74 0.918 –0.71 Age of oldest child 2.497 1.30 1.451 0.57 1.019 0.03 Spouse aged 60–69 Spouse aged 70+ Spouse, some primary sch. Spouse, compl. primary Spouse, compl. junior high Divorced/widowed/never married Max. education of children Working Spouse working Child working Any ADL problem Poor GHS Rural 2.349 2.27 1.693 1.45 2.120 1.92 Number of observations 1,783 Likelihood ratio (Chi-squared) 245.4 p-value 0.000 Pseudo R-squared 0.073 NOTES: The sample consists of males aged 55−84 in 2007 of whom 1993 data are available and who had at least one living child in 1993. Relative risk ratios are reported. Province dummy variables are included in the regressions but not reported. * denotes p < 0.05; ** p < 0.01. SOURCE: Author’s calculation based on data from Indonesia Family Life Survey, IFLS1 and IFLS4.

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257 FIRMAN WITOELAR Specification 2 with an adult with spouse child other RRR Z RRR Z RRR Z 0.460 –1.53 0.155* –2.35 0.389 –1.83 0.500 –1.26 0.116** –2.85 0.358 –1.81 1.459 0.80 0.976 1.65 2.224 1.64 1.046 0.09 0.901 1.14 1.458 0.69 1.333 0.40 2.593 1.95 2.101 1.01 0.963 –0.31 0.148 1.84 0.919 –0.65 0.831 –1.53 0.121 0.36 0.913 –0.73 2.231 1.11 0.865 0.36 0.982 –0.03 1.324 0.35 0.954 0.23 1.500 0.48 0.135 –1.48 0.000 –0.00 0.627 –0.36 0.773 –0.60 0.309 –0.73 0.976 –0.05 1.137 0.23 0.645 0.28 1.102 0.16 1.608 0.56 1.121 0.39 1.857 0.71 0.030** –2.81 0.154* –2.09 0.184 –1.87 1.056 1.31 0.042 0.79 1.012 0.26 0.814 –0.35 0.493 –0.26 1.213 0.30 1.226 0.56 0.285 –0.61 0.891 –0.31 0.933 –0.17 0.523 0.72 1.086 0.19 1.342 0.48 0.760 0.40 1.246 0.34 0.711 –0.63 0.443 –0.30 0.885 –0.22 2.601 2.41 0.726 1.67 2.238 1.96 1,783 299.08 0.000 0.089

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258 AGING IN ASIA APPENDIX TABLE 10-A2 MNL of Living Arrangements, 1993–2007, Females, 55+, in 2007 (living alone is the base outcome) Specification 1 with an adult with spouse child other RRR Z RRR Z RRR Z Aged 60-69 0.588* –2.10 0.465*** –3.43 0.654 –1.52 Aged 70+ 0.237*** –5.04 0.294*** –5.22 0.632 –1.56 Some primary school 1.699 2.19 1.474 1.91 1.696* 2.09 Completed primary 1.402 1.18 1.237 0.90 1.110 0.34 Completed junior high 2.148* 2.05 1.902* 2.01 1.299 0.61 # of living sons 0.908 –1.54 1.123* 2.35 0.875* –2.01 # of living daughters 1.098 1.39 1.327*** 5.09 1.202** 2.68 Age of oldest child 0.386 –0.85 0.448 –0.76 0.695 –0.29 Spouse aged 60−69 Spouse aged 70+ Spouse, some primary sch. Spouse, compl. primary Spouse, compl. junior high Divorced/widowed/never married Max. education of children Working Spouse working Child working Any ADL problem Poor GHS Rural 1.536 2.03 0.908 –0.57 1.128 0.54 Number of observations 2,018 Likelihood ratio (Chi-squared) 585.1 p-value 0.000 Pseudo R-squared 0.138 NOTES: The sample consists of females aged 55−85 in 2007 of whom 1993 data are avail - able and who had at least one living child in 1993. Relative risk ratios are reported. Prov - ince dummy variables are included in the regressions but not reported. * denotes p < 0.05; ** p < 0.01; *** p < 0.001. SOURCE: Author’s calculation based on data from Indonesia Family Life Survey, IFLS1 and IFLS4.

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259 FIRMAN WITOELAR Specification 2 with an adult with spouse child other RRR Z RRR Z RRR Z 1.081 0.28 0.156 –1.82 0.998 –0.01 1.183 0.49 0.157 –2.05 1.462 1.10 1.160 0.56 0.245 0.57 1.394 1.24 0.897 –0.33 0.208 –0.85 0.825 –0.56 1.382 0.70 0.441 0.38 0.918 –0.17 0.836** –2.65 0.054 0.37 0.835** –2.60 1.058 0.80 0.073*** 4.13 1.179* 2.34 0.313 –1.01 0.268 –1.29 0.557 –0.46 0.404*** –3.34 0.112** –3.16 0.394** –3.14 0.155*** –4.34 0.117** –3.14 0.256** –3.20 1.152 0.50 0.307 0.85 1.179 0.54 2.002 1.94 0.646* 2.26 1.733 1.42 0.813 –0.48 0.451 0.61 1.124 0.25 0.010 –4.39 0.252 –0.50 0.597 –1.36 1.042 1.73 0.021*** 3.66 1.019 0.74 0.982 –0.09 0.116* –2.16 0.524** –3.07 3.544*** 4.42 0.440** 2.97 2.176** 2.63 1.075 0.31 0.354 3.58 1.180 0.71 1.069 0.27 0.191 –0.12 1.104 0.40 0.540 –2.01 0.145 –2.04 0.748 –0.99 1.437 1.56 0.207 0.64 1.254 0.96 2,016 590.2 0.000 0.139

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