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13

Socioeconomic Success and Health in Later Life: Evidence from the Indonesia Family Life Survey1

Firman Witoelar, John Strauss, and Bondan Sikoki

Indonesia has been undergoing a health and nutrition transition over the past 20 years and more. Overall, health of the population has been improving, as indicated by a continuing rise in attained adult heights for men and women over the entire 20th century (heights of both men and women increased by about 1 cm per decade over this period, Strauss and Thomas, 1995; Strauss et al., 2004). In Indonesia, infectious diseases caused 72% of all deaths in 1980; by 1992, noninfectious conditions caused more than half of the country’s deaths (Indonesian Public Health Association, 1993). As part of the reason for the increase in deaths from chronic conditions, body mass indices (BMIs) have been rising for middle-aged people and the elderly, as has been noted more generally in Asia (see, for example, Monteiro et al., 2004; Popkin, 1994; Strauss and Thomas, 2008; Strauss et al., 2004). In Indonesia, body mass among the aged population has been rising rapidly, especially for women, as has waist circumference. On the other hand, hemoglobin levels have also been rising, though from low levels, leading to improved health. Yet, other health measures have been fairly steady as shown in the Indonesia Family Life Survey (IFLS),

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1 An earlier version of this chapter was presented at the annual meetings of the Population Association of America, April 2009, Detroit, Michigan, and the World Congress of the International Union for the Scientific Study of Population, October 2009, Marrakesh, Morocco. We thank Paul Heaton, James P. Smith, and other participants of the RAND Labor and Population Workshop for their very helpful comments. All errors are ours. Witoelar gratefully acknowledges the financial support of the World Bank’s Research Support Budget. The views expressed here do not necessarily reflect those of the World Bank and of its member countries.



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13 Socioeconomic Success and Health in Later Life: Evidence from the Indonesia Family Life Survey1 Firman Witoelar, John Strauss, and Bondan Sikoki I ndonesia has been undergoing a health and nutrition transition over the past 20 years and more. Overall, health of the population has been improving, as indicated by a continuing rise in attained adult heights for men and women over the entire 20th century (heights of both men and women increased by about 1 cm per decade over this period, Strauss and Thomas, 1995; Strauss et al., 2004). In Indonesia, infectious diseases caused 72% of all deaths in 1980; by 1992, noninfectious conditions caused more than half of the country’s deaths (Indonesian Public Health Associa- tion, 1993). As part of the reason for the increase in deaths from chronic conditions, body mass indices (BMIs) have been rising for middle-aged people and the elderly, as has been noted more generally in Asia (see, for example, Monteiro et al., 2004; Popkin, 1994; Strauss and Thomas, 2008; Strauss et al., 2004). In Indonesia, body mass among the aged population has been rising rapidly, especially for women, as has waist circumference. On the other hand, hemoglobin levels have also been rising, though from low levels, leading to improved health. Yet, other health measures have been fairly steady as shown in the Indonesia Family Life Survey (IFLS), 1 An earlier version of this chapter was presented at the annual meetings of the Population Association of America, April 2009, Detroit, Michigan, and the World Congress of the Inter- national Union for the Scientific Study of Population, October 2009, Marrakesh, Morocco. We thank Paul Heaton, James P. Smith, and other participants of the RAND Labor and Population Workshop for their very helpful comments. All errors are ours. Witoelar gratefully acknowledges the financial support of the World Bank’s Research Support Budget. The views expressed here do not necessarily reflect those of the World Bank and of its member countries. 309

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310 AGING IN ASIA including the prevalence of hypertension. In terms of measures of health outcomes, while some trends seem upwards, specifically the movement out of undernutrition and communicable diseases, there seems at the same time to have been a movement toward more risk factors that are likely to lead to future chronic problems, but not universally so. Related to this, other symptoms, such as low levels of HDL cholesterol, are very high, especially for men, and the extremely high rate of current male smoking does not yet show a downward trend (Witoelar, Strauss, and Sikoki, 2009). The backdrop of these important health and nutrition transitions is a country where the formal social safety system is still in its infancy and policies and programs that are designed to address specific challenges brought about by an aging population are still lacking.2,3 As in many developing countries in the region, elderly in Indonesia mostly rely on children and family networks for old-age support, either through co- residency or transfers (Cameron and Cobb-Clark, 2008). With the elderly population becoming more exposed to risk of chronic and noncommuni- cable diseases, the issues of elderly care are becoming increasingly impor- tant.4 Understanding the socioeconomic status (SES) correlates of elderly health outcomes will help to improve knowledge that could be useful in designing health as well as social programs to improve the well-being of the elderly in Indonesia. In this chapter, we document the health and nutrition transition that the elderly population in Indonesia has undergone in the 15 years between 1993 and 2008, using the four full waves of the Indonesia Family Life Survey (IFLS).5 This period spans a period of rapid economic growth from 1993 to 1997, a major financial crisis starting at the end of 1997 going through 1998 and 1999, and a major economic expansion starting in 2000, continuing through early 2008. IFLS is uniquely suited to look at changes over time, both for age groups and for birth cohorts in Indonesia, as it is a panel survey covering most of the country. Indonesia, like other developing countries in Asia and Latin America, has been aging rapidly. In 1980, only 3.4% of the population was aged 65 and older; by 2010, it was projected to be 6.1%, and by 2040, 14.7% (Kinsella and He, 2009). The population aged 65 and older is projected to double between 2000 and 2020 and again by 2040. We examine the IFLS sample 45 years and older 2 An important bill on social safety nets was passed in October 2004 (Law no 40/2004). It includes a number of provisions that are important for elderly such as pensions, old-age savings, and health coverage, but has yet to be implemented. 3 See Abikusno (2009) for a review of past and recent laws and government policies related to elderly in Indonesia. 4 Van Eeuwijk (2006) argues that the epidemiological health transition has necessitated a shift from a “cure” to “care” paradigm in healthcare delivery in urban areas in Indonesia. 5 IFLS1 was fielded in 1993, IFLS2 in 1997, IFLS3 in 2000, and IFLS4 in 2007–2008.

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311 FIRMAN WITOELAR, JOHN STRAUSS, and BONDAN SIKOKI in each of the four waves, pretending that we have a series of independent cross-sections. Age 45 is chosen because it corresponds to early retirement age in Indonesia and is the age cutoff used in the new Health and Retire - ment Study type surveys being done in Asia.6 We focus in this chapter on examining changes over time for a series of health outcomes and behaviors, mainly using biomarkers. The health outcomes that we focus on are body mass index (BMI), waist circumfer- ence (a measure of body fat, given BMI), blood hemoglobin, total and HDL cholesterol levels, hypertension, cognition measured by word recall, and an index of depression (the short CES-D).7 This is a much broader set of health indicators than is usually analyzed, in large part because such a rich set of health data is not usually available in broad-purposed socioeconomic surveys. In addition to looking at trends in IFLS, we examine the correlations between these health outcomes and behaviors, and a series of SES variables: own education and the log of household per-capita expenditure (pce). In all cases, we examine the data separately for men and women and include age, period, and cohort effects (normalized), as well as dummy variables for province and rural area, alone and interacted with year of survey. We find that the nutrition transition has progressed strongly in Indone- sia over the 15-year period 1993–2008. Large increases in overweight have occurred for both men and women aged 45 years and older. For women, a full 33% are now overweight and for men, 10 percentage points less. On the other side of the coin, underweight has dramatically decreased, although among the current older population, it is still a problem. Related to nutrition, blood hemoglobin has improved over this period, especially since 2000. However, levels of hemoglobin are still low by international standards. On the other hand, hypertension has been constant over the period since 1997, since IFLS has been measuring it. We find strong, positive correlations between SES and good health outcomes in most cases except hypertension and cholesterol. We recog- nize that causality runs both ways. We allow for interactions between one SES variable—education—and age and find that education tends to suppress the negative impact of age on many health outcomes. For hyper- tension we have data not only on measured prevalence, but also on doctor 6 These are the China Health and Retirement Longitudinal Study (CHARLS), Japanese Study of Aging and Retirement (JSTAR), Korean Longitudinal Study of Ageing (KLoSA), and Longitudinal Study of Aging in India (LASI). 7 We also examine the degree to which older Indonesians have difficulties with Activities of Daily Living and Instrumental Activities of Daily Living (ADLs and IADLS, respectively) and their self-reported general health status, as well as two important inputs for elderly health: smoking and physical activity. The results are available in Witoelar, Strauss, and Sikoki (2009).

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312 AGING IN ASIA diagnosis. We find a very high level of underdiagnosis of hypertension, which is strongly, negatively associated with SES for women, but not associated for men. Even among those who have been diagnosed, a large proportion claim not to be taking medications. We speculate that for other chronic health conditions, the degree of underdiagnosis is likely to also be quite high, suggesting the need for major health campaigns directed both at the general population and very specifically at doctors and other health providers. DATA AND METHODOLOGY The Indonesia Family Life Survey is a general-purpose survey designed to provide data for studying many different behaviors and outcomes. The survey contains a wealth of information collected at the individual and household levels, including indicators of economic and noneconomic well- being. In particular for this chapter, IFLS collects a rich set of information on health outcomes, in particular on many biomarkers. IFLS is an ongoing longitudinal survey. The first wave, IFLS1, was conducted in 1993−1994. The survey sample represented about 83% of the Indonesian population living in 13 of the country’s 26 provinces.8 IFLS2 followed up with the same sample four years later, in 1997. IFLS2 ended in December 1997, just as the financial crisis was beginning, so it serves as an immediate baseline. IFLS3 was fielded on the full sample in 2000, three years after the crisis, and IFLS4 in 2007−2008, some 10 years after. Thus, IFLS from 1993 to 2008 provides a period of still-strong economic growth, followed by a major economic crash and recovery. In this chapter, for some purposes we treat each year as though it were an independent cross-section, in order to explore how prevalence of different measures have evolved cross-sectionally for a particular age group, those aged 45 and older. For the regressions, though, we test pooling across years and then pool with some interactions after we fail to reject that SES coefficients are the same over the four waves. We do not employ dynamic models in this chapter and so do not use the panel nature directly; we will deal with these topics in another chapter. One potential worry in a study like this over a 15-year period is sample 8 Public-use files from IFLS1 are documented in six volumes under the series title The 1993 Indonesian Family Life Survey, DRU-1195/1–6-NICHD/AID, The RAND Corporation, December 1995. IFLS2 public-use files are documented in seven volumes under the series The Indonesia Family Life Survey, DRU-2238/1-7-NIA/NICHD, RAND, 2000. IFLS3 public-use files are documented in six volumes under the series The Third Wave of the Indonesia Family Life Survey (IFLS3), WR-144/1-NIA/NICHD. IFLS4 public-use files are documented in the six volumes under the series The Fourth Wave of the Indonesia Family Life Survey (IFLS4), WR-675/1-NIA/NICHD.

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313 FIRMAN WITOELAR, JOHN STRAUSS, and BONDAN SIKOKI attrition. However, the attrition in IFLS is quite low. In IFLS1, 7,224 house- holds were interviewed, and detailed individual-level data were collected from over 22,000 individuals. In IFLS2, 94.4% of IFLS1 households were re-contacted (interviewed or died). In IFLS3, the re-contact rate was 95.3% of IFLS1 dynasty households (any part of the original IFLS1 households). In IFLS4, the recontact rate of original IFLS1 dynasties was 93.6% (of course, the period between waves was seven years, not three). Among IFLS1 dynasties, 90.3% were either interviewed in all four waves or died. Of some 6,523 households, 6,329, or 87.6%, were actually interviewed in all four waves.9 These re-contact rates are as high as or higher than most longitudinal surveys in the United States and Europe. For the regressions, we do not weight, but for the descriptive tables we do weight, both for the sampling procedures (which oversampled urban areas and some outer provinces) and for attrition (see Strauss et al., 2009, for details of weight- ing). The weights provide the inverse probability that a household and individual were sampled and appeared in IFLS in each wave. To look at the associations of SES and health outcomes under a multi- variate context, we run a set of regressions. The specification, which is used for all health outcomes analyzed in this chapter, is as follows. In results not shown, we first test for pooling across waves, for those health outcomes that we have data for multiple waves. We find that the age, schooling, and pce coefficients are not significantly different across years although the province/rural-urban dummies are (results are available upon request). Consequently, we pool the data across rounds of the sur- vey (IFLS1, 2, 3, and 4), but allow for interactions between year dummies and the province/rural-urban dummies. These interactions will capture community/time differences in prices, healthcare availability and quality, and health conditions. The sample for each regression consists of adults who are aged 45 and older at the time of the survey, and for whom the physical measurements (or other measures) are available. Estimation for males and females are done separately. We use ordinary least squares for continuous dependent variables and linear probability (LP) model for binary dependent variables. LP model estimates are consistent for estimating average partial effects of the regressors, which is what we are interested in. Robust standard errors of the regression coefficients are computed, which also allow for clustering at the community level. By using robust standard errors for the linear probability regressions, we ensure that these standard error estimates are consistent. Table 13-1 shows means and standard deviations from the IFLS4 data for our covariates. We create dummy variables for age indicating whether an individual is aged 55 and older, 65 and older, and 75 and older. In this 9 See Thomas et al. (forthcoming) for a more detailed discussion of IFLS attrition rates.

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314 AGING IN ASIA TABLE 13-1 Descriptive Statistics of the Socioeconomic Variables, 2007 Male (N = 4,014) Female (N = 4,629) Variable Mean Std. Dev Mean Std. Dev Age (yrs) 57.94 10.465 58.47 10.786 Age (dummy vars.): 45–54 0.466 0.499 0.455 0.498 55–64 0.272 0.445 0.261 0.439 65–74 0.178 0.383 0.191 0.393 75+ 0.084 0.277 0.093 0.291 Years of education (yrs) 6.046 4.661 4.015 4.273 School completion (dummy vars.) No schooling 0.150 0.357 0.337 0.473 Some primary school 0.281 0.450 0.290 0.454 Completed primary school 0.276 0.447 0.206 0.405 Completed junior high 0.293 0.455 0.167 0.373 Monthly pce (Rp) 562,568 834,740 590,319 1,064,606 Residence (dummy vars.) Rural 0.503 0.500 0.492 0.500 Province North Sumatra 0.055 0.229 0.061 0.239 West Sumatra 0.049 0.216 0.055 0.229 South Sumatra 0.047 0.211 0.045 0.208 Lampung 0.044 0.205 0.036 0.187 Jakarta 0.064 0.245 0.060 0.238 West Java 0.166 0.372 0.157 0.364 Central Java 0.138 0.345 0.145 0.352 Yogyakarta 0.072 0.259 0.074 0.261 East Java 0.155 0.362 0.162 0.369 Bali 0.054 0.226 0.054 0.226 West Nusa Tenggara 0.059 0.236 0.058 0.234 South Kalimantan 0.047 0.211 0.040 0.195 South Sulawesi 0.049 0.215 0.052 0.223 SOURCE: Data from IFLS4. way, the coefficients on the dummy variables indicate the marginal change from the next lowest age group (not from the omitted group) of being in the reference group. Similarly, for education, the dummy coefficients show the marginal change over the next lowest education group: We use a dummy variable for having at least some primary education, completed primary school or more, and completed junior high school or more. For per capita expenditures (pce), we take logs and then use a linear spline with a knot at the median of log pce.10 For health measures that we have 10 Thecoefficient on the second log pce variable we report is the change in the coefficient from the slope to the left of the knot point.

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315 FIRMAN WITOELAR, JOHN STRAUSS, and BONDAN SIKOKI data on from more than one wave, we include dummy variables if the observations are from 1997 and after (if 1993 observations are available), 2000 and after, or 2007, and, as stated, interaction of these period effects with province and province-rural dummies. For the few health variables that we only have data for 2007–2008, we just include the province and province-rural dummies. Also for measures that data exist for multiple waves, we use five-year birth cohort dummy variables.11,12 It is, of course, not possible to separately identify age, cohort, and period effects without untestable assumptions made. In our case, we aggregate ages into 10-year intervals and birth cohorts into five-year groups.13 Because we are pooling the four waves for each age group, we have several birth-year cohorts, helping identification. Nevertheless, we are not so interested in the age, cohort, or year effects as we are in the SES coefficients. However, if we left out age and/or birth cohort variables, we would bias the education coefficients positively, as the estimated education impacts would then also capture cohort effects. This would arise because younger birth cohorts have more schooling and also faced better health conditions when they were babies and in the fetus, compared to older cohorts. There is an accumulation of evidence now that better health conditions when young are associated with better health in old age (for instance, Barker, 1994; Gluckman and Hanson, 2005; and Strauss and Thomas, 2008, for an economist’s perspective). We have to be careful not to interpret the SES coefficients from these regressions as causal (Strauss and Thomas, 1995, 1998, 2008). Causality runs in both directions between SES and health outcomes. However, we add one variable that can help some in this regard, an interaction between years of education and de-meaned age. Using de-meaned age is helpful for interpretation because then the coefficients on the education dummies show the differentials at the sample mean age. The interaction coefficient then shows how that differential changes with age differences compared to the mean age. What we are looking for is whether educa- tion mitigates the powerful negative influence of aging on our health outcomes. If it does, then this is more consistent with a causal interpreta - 11 The birth-year cohort dummy variables included are as follows: –1928, 1929–1933, 1934–1938, 1939–1943, 1944–1948, 1949–1953, 1954–1958, with 1959–1963 omitted as the base. 12 For health measures that we only have data from 2007, of course, we do not use either year or birth cohort dummies, but we still use the age dummies. For these cases, the age dummies must be interpreted with even more caution, since it is not possible to disentangle age from birth cohort from time effects. 13 The year dummy variables are aged 55 and older, 65 and older, 75 and older, with 45 and older omitted as the base.

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316 AGING IN ASIA tion of our education coefficients.14,15 Studies of child height have shown that mother’s education has its largest impact on heights when the child is less than three years (Barrera, 1990; Thomas, Strauss, and Henriques, 1990, 1991). This is thought to be the period during which children are most vulnerable to infection from dirty water and ill-prepared food, so that mother’s schooling might well have its biggest impact during that period. Among the mechanisms for this enhanced impact is thought to be an allocative efficiency effect of mother’s schooling, knowing, or being better at acquiring information regarding what inputs are better and safer for children, such as boiling water. A similar argument might be applied to our measures of health, which are largely general; at older ages, people are more susceptible to problems, hence one’s own schooling may have a larger allocative impact at these ages (though possibly from affecting health inputs and behaviors from years earlier). RESULTS Physical Measurement: Anthropometry, Hemoglobin Level, and Hypertension BMI We first look at a number of biomarkers: BMI, waist circumference, blood hemoglobin levels, and hypertension.16 BMI, which is weight (in kg) 14 While this interaction coefficient could also represent a nonlinear effect of schooling, the fact that we enter schooling with level dummies protects us in part against this potential confounding effect. 15 Another empirical strategy we could have taken would be to include household fixed effects. That would have captured all factors at the household level, but still would not have addressed the issue of unobserved individual factors. Household fixed effects would have required there to be multiple men aged 45 and older within the same household and likewise for adult women. We examined the cell sizes for our samples, using as our defini - tion of household, the “dynastic” 1993 households (that is combining all households that split from a given 1993 household into one household). We found that an average dynastic household contained 1.1 adult male or adult female members aged 45 and older. In the case of CES-D, for example, we had 3,900 individual men in our sample and 3,683 dynasties. That means we only had 217 individuals from multiple member households, and it is this group that would be used to estimate the SES coefficients. We judged that this was too small a group from which to reliably get estimates. This case is typical. For health outcomes that we measure over time, like BMI, we have numerous persons for whom we have multiple measures over waves. We thus could have used individual fixed effects in that case, but that should be part of a dynamic analysis, which is a different research exercise than this chapter. 16 Heights were measured using a lightweight SECA aluminum height board, the SECA 214 portable stadiometer. Weights were measured using a portable digital scale, the CAMRY EB6171. Hemoglobin was measured using a small, hand-held meter, the Hemocue Hb301 analyzer. A finger prick was made using a lancet and a drop of blood inserted into the Hemocue microcuvette. Blood pressure was taken with a digital meter, the Omron HEM 712c meter. Total and HDL cholesterol were measured using a CardiochekPA meter. This meter measures over the range 100–400 for total cholesterol and 15–100 for HDL.

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317 FIRMAN WITOELAR, JOHN STRAUSS, and BONDAN SIKOKI divided by height (in m) squared, provides a convenient summary of height and weight of adults. We use World Health Organization (WHO) standards whereby adults whose BMI is under 18.5 are considered under- nourished and those whose BMI is 25 or greater are considered over- weight. Extreme values of BMI are associated with elevated hypertension, diabetes, and other causes of mortality. Figures 13-1a and 13-1b plot the cumulative distribution functions (CDF) of BMI for adult males and females aged 45 and older using data from IFLS1, 2, 3, and 4. The CDFs are shifted down for each year after 1993 for both men and women. The shift for 2007 from 2000 is especially large. The fact that the CDFs do not cross means that each successive year first order stochastically dominates the last. In the case of BMI, unlike income, stochastic dominance across the entire distribution does not have a clear welfare implication. On the one hand, undernourishment is unambigu- ously dropping, but on the other hand, overweight is unambiguously increasing. Table 13-2 shows the percentages of adults aged 45 and older who are undernourished and overweight in 2007. The percentage of adult males who are undernourished was around 17.5 in 2007. This number continues the decline from 28.3 in 1993 to 23.5 in 2000 (see Witoelar, Strauss, and Sikoki, 2009).17 The numbers are similar for women, with around 17.4% who were undernourished in 2007, compared to 29.7% in 1993. But what is more interesting has to do with the proportion of those overweight. In 2007, around 31% of elderly women have BMI 25 or over, more than double the fraction of 1993. Among elderly men in 2007, 17% are overweight, compared to 8.5% in 1993. Among the different age groups, it is the 45−54 age group who have both the lowest fraction of undernourished and the largest fraction of overweight. The increase over the years and the substantial degree of overweight suggests that overnutrition and health conditions associated with it have become increasingly important in Indonesia. At the same time, under- nutrition has not entirely disappeared, though its magnitude among the aged has sharply dropped. Holding BMI constant, greater waist circumference increases the risks of various cardiovascular diseases. For people who are overweight or obese, the risk of future mortality is higher if their waist circumference is greater than 120 cm for men or 88 cm for women. The CDF of waist circumference for both men and women shifted to the right between 2000 and 2007 (see Witoelar, Strauss, and Sikoki, 2009). Around 30% of women aged 45 and older in 2007 had waist circumferences that are greater than 88 cm compared to around 20% in 2000. This CDF does not control for BMI changes, so a lot of the increase in waist circumference may simply be 17 Percentages of adults 45+ who are undernourished and overweight in all four survey waves of the IFLS are presented in Witoelar, Strauss, and Sikoki (2009).

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A. Males, aged 45+ B. Females, aged 45+ 318 1 1 .8 .8 .6 .6 .CDF .4 .4 .2 .2 0 0 10 15 18.5 20 25 30 35 10 15 18.5 20 25 30 35 BMI BMI 1993 1997 2000 2007 1993 1997 2000 2007 C. Males, aged 45+ D. Females, aged 45+ Waist (cm) 50 60 70 80 90 100 110 120 50 60 70 80 90 100 110 120 12 14 16 18 20 22 24 26 28 30 32 34 36 12 14 16 18 20 22 24 26 28 30 32 34 36 BMI BMI 2000 2007 2000 2007 FIGURE 13-1 CDF of body mass index and waist circumference by body mass index, adults aged 45 and older. SOURCE: Data from IFLS, Waves 1-4. R02177

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319 FIRMAN WITOELAR, JOHN STRAUSS, and BONDAN SIKOKI TABLE 13-2 Under/Overnutrition, Low Hemoglobin Level, High Total Cholesterol, and Low HDL Level Among 45+ in 2007 Male Female % # obs. % # obs. 45−54 years % undernourished 9.46 1,870 9.39 2,106 % overweight 22.65 1,870 40.18 2,106 % low blood hemoglobin 17.72 1,869 28.08 2,091 % high total cholesterol 12.49 1,832 20.02 2,054 % low HDL 70.59 1,832 40.81 2,054 55−64 years % undernourished 18.22 1,096 16.64 1,211 % overweight 17.26 1,096 30.57 1,211 % low blood hemoglobin 26.01 1,093 32.82 1,212 % high total cholesterol 12.66 1,071 26.00 1,204 % low HDL 64.79 1,071 36.25 1,204 65−74 years % undernourished 27.95 713 29.57 878 % overweight 8.59 713 18.82 878 % low blood hemoglobin 40.86 728 40.25 886 % high total cholesterol 9.48 716 23.07 884 % low HDL 59.49 715 41.31 883 75+ years % undernourished 38.05 338 33.60 438 % overweight 6.31 338 13.96 438 % low blood hemoglobin 52.24 350 50.06 461 % high total cholesterol 8.60 339 21.63 448 % low HDL 65.22 339 34.47 448 All adults 45+ % undernourished 17.54 4,017 17.40 4,633 % overweight 17.31 4,017 31.14 4,633 % low blood hemoglobin 27.12 4,040 33.81 4,650 % high total cholesterol 11.66 3,958 22.33 4,590 % low HDL 66.55 3,957 39.09 4,589 Mean BMI 21.75 4,017 22.90 4,633 Mean blood hemoglobin 13.99 4,040 12.42 4,650 Mean total cholesterol 178.16 3,958 198.46 4,590 Mean HDL 34.94 3,957 44.97 4,589 SOURCE: Data from IFLS4. Undernourished = BMI < 18.5, overweight = BMI ≥ 25, low blood hemoglobin = < 13.0 mg/dL (male) or < 12.0 mg/dL (female), high total cholesterol = (≥ 240 mg/dL, low HDL = < 40 mg/dL).

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331 FIRMAN WITOELAR, JOHN STRAUSS, and BONDAN SIKOKI TABLE 13-5 Hypertension, Underdiagnosis, and Medication Take Up, Age 45+ PANEL A. Incidence of hypertension, diagnosis, and medication take up Men Women 1997 2000 2007 1997 2000 2007 Observations 2856 3,477 4,044 3,307 3,631 4,674 % measured as hypertensive 43.8 44.2 44.2 52.6 49.6 52.7 % not measured, but diagnosed 6.5 11.6 as hypertensivea Total hypertensive (%)b 43.8 44.4 50.7 52.6 50 63.3 PANEL B. Underdiagnosis of hypertension by completed education, adults 45+c Education 2007 2007 no schooling 79.0 69.5 primary schooling 74.4 58.2 junior high 73.2 52.1 senior high + 68.0 62.1 all adults 45+ 73.6 62.1 PANEL C. Hypertensive and not taking medication, by completed education, aged 45+d 2007 2007 Education no schooling 91.5 92.6 primary schooling 89.1 89.3 junior high 73.6 79.4 senior high + 77.9 86.8 all adults 45+ 85.0 89.2 aDiagnosed” if answered “Yes” to the question “Has a doctor/nurse/paramedic ever told you that you have hypertension?” The question was only asked in 2007. Percentages of those diag- nosed with hypertension are out of individuals aged 45+. bPercentages are out of individuals 45+. cPercentages are out of individuals 45+ who are measured and/or diagnosed to be hyper- tensive. dPercentages are out of individuals 45+ who are diagnosed to be hypertensive. SOURCE: Data from IFLS Waves 2–4. medications and the percentage is higher for women, 89%, than for men, 85%. Education level gradients exist, particularly for men. The multivariate regressions presented in Table 13-6 confirm what we saw in Figure 13-4 that the probability of having hypertension increases with age, although the increase with birth cohort is even larger. For men, education levels are jointly significant, with higher levels of schooling

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TABLE 13-6 Hypertension and Underdiagnosis of Hypertension: Linear Probability Models 332 Hypertension Underdiagnosis of hypertension Male Female Male Female Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat Age Group (dummy variables) 55 and older 0.0478** [2.434] 0.0390** [2.143] –0.0290 [–0.910] 0.0310 [1.152] 65 and older 0.0257 [1.230] 0.0558*** [3.115] –0.0351 [–1.111] 0.0200 [0.692] 75 and older 0.0695*** [2.634] 0.0300 [1.372] –0.0259 [–0.684] –0.0289 [–0.892] Years of Education (dummy variables) At least some primary –0.0067 [–0.385] 0.0105 [0.680] –0.0144 [–0.396] –0.1249*** [–4.704] Completed primary school or more 0.0255* [1.658] –0.0018 [–0.113] –0.0213 [–0.736] 0.0287 [1.030] Completed junior high or more 0.0333** [2.172] –0.0060 [–0.286] –0.0358 [–1.075] 0.0298 [0.872] Education × Age Interaction Years of education × agea –0.0001 [–0.984] 0.0004*** [2.984] –0.0003 [–1.334] –0.0003 [–1.250] Per Capita Expenditures (splines)b 0 - median pce 0.0172 [1.095] 0.0371** [2.540] –0.0507 [–1.342] –0.0998*** [–3.244] >= median pce 0.0022 [0.096] –0.0285 [–1.397] 0.0263 [0.484] 0.0753* [1.652] Year Dummy Variables 2000 and after 0.0580 [1.610] 0.0385 [1.068] 2007 0.1166*** [2.674] 0.0608 [1.401] Constant –0.0267 [–0.147] –0.1018 [–0.610] 1.3425*** [2.802] 1.7300*** [4.411] Observations 10,376 11,994 1,966 2,745 R-squared 0.064 0.088 0.045 0.072 Cohort Dummy Variables Yes Yes No No Province × Rural Dummy Variables + Yes Yes Province × Province × Province × Rural × Year Interactions rural rural

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F-tests for Joint Significance: Age group dummy variables 3.447 0.017 3.630 0.013 1.163 0.323 0.988 0.398 Education variables 4.206 0.006 0.171 0.916 1.280 0.281 7.586 0.000 Educ. years + educ. age interactions 3.953 0.004 2.913 0.021 1.253 0.288 7.305 0.000 Per capita expenditures 2.569 0.078 4.424 0.012 1.891 0.152 8.253 0.000 Cohort dummy variables 7.374 0.000 4.417 0.000 Year dummy variables 4.791 0.009 1.990 0.138 Province x rural dummy variables 1.769 0.019 3.293 0.000 2.653 0.000 6.955 0.000 Year x prov x rural variables 1.979 0.000 2.682 0.000 interactions NOTES: The dependent variable for the hypertension regressions is whether the individual is hypertensive = 1, 0 otherwise; and for the under - diagnosis of hypertension, the dependent variable is 1 if the individual has ever been diagnosed with hypertension, 0 otherwise, conditional of being hypertensive. Blood pressure measurement was not collected in 1993, and question about diagnosis was only asked in 2007. t-statistics (in brackets) are based on standard errors that are robust to clustering at the community level. * denotes significant at 10%; ** significant at 5%; *** significant at 1%. The omitted group for age dummy variable is 45 and older, for education, “no schooling,” and for province, Jakarta. Birth- year cohort dummy variables included are as follows: –1928, 1929–1933, 1934–1938, 1939–1943, 1944–1948, 1949–1953, 1954–1958, with 1959–1963 omitted. aThe interaction term is between years of education and the de-meaned age. Means of age in the hypertension sample are: 58.3 (male), 58.9 (fe - male); in the underdiagnosis sample: 60.3 (male), 60.8 (female). b Knot point is at the median pce, coefficient represent change in the slope. SOURCE: Data from IFLS, Waves 2-4. 333

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334 AGING IN ASIA associated with a great likelihood of being hypertensive. Pce on the other hand is not significantly related to hypertension for men. For women, it is pce and not education levels that is significantly related to hyper- tension, with higher pce being associated with higher chances of having hypertension. For underdiagnosis of hypertension, the regression results suggest that among women with hypertension, having some primary education reduces the probability of being underdiagnosed compared to those with no schooling, although having higher levels of school- ing show no additional effects. The education variables are jointly sig- nificant for both women and men. On the other hand, pce is significant at under 1% for women. The higher per capita expenditure, the lower is underdiagnosis, so underdiagnosis is larger for lower income and uneducated people, particularly women. Cognition: Word Recall Cognition has been found to be an important issue among the elderly (see McArdle, Fisher, and Kadlec, 2007). We use immediate and delayed word recall as one of the cognitive measures, namely the episodic mem- ory measure. In IFLS4, like the U.S. Health and Retirement Study (HRS), respondents are read a list of 10 simple nouns, and they are immediately asked to repeat as many as they can, in any order. After answering unre - lated questions on morbidity, maybe 10 minutes later, the respondents are then asked again to repeat as many words as they can. We use the average number of correctly immediate and delayed recalled words as our memory measure (McArdle, Smith, and Willis, 2009). On average, elderly men are able to recall 2.9 words, and elderly women are able to recall 3.2 words. Figure 13-5a shows a strong negative binary relationship between the number of words recalled and age. Note that in the top panel, the line for men is higher than that of women. This is partly due to the fact that at any given age, men on average are better educated than women. Along the same lines, part of the reason that the lines coincide is that for any given years of education, men are typically older than women. The multivariate analysis, presented in Table 13-7, sheds more light on these associations. The regressions show a strong negative relationship between age and memory for men and women. A strong, positive relationship between education and memory is also evident, with a negative coefficient on the age-schooling interaction term for men, suggesting that education reinforces the negative effects of aging on memory in this case. The pce variables are jointly significant, positively correlated with word recall, with the effect at low levels of pce for men and at high levels for women.

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335 FIRMAN WITOELAR, JOHN STRAUSS, and BONDAN SIKOKI A. Words recalled and age 4 Words Recalled 3 Male Female 2 1 40 50 60 70 80 Age B. CES-D score and age 5.5 5 CES-D Score Male 4.5 Female 4 3.5 40 50 60 70 80 Age FIGURE 13-5 Words recalled and CES-D 10 (2007), adults aged 45 and older. SOURCE: Data from IFLS4. R02177 Figure 13-5 parts a and b combined to fit on one page vectors, editable

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TABLE 13-7 Multivariate Regressions: Number of Words Recalled and the CES-D 10 Score 336 Word Recall CES-D 10 Male Female Male Female Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat Age Group (dummy variables) 55 and older –0.2597*** [–3.530] –0.2800*** [–4.455] 0.1468 [1.105] 0.0425 [0.292] 65 and older –0.4163*** [–4.962] –0.4913*** [–6.805] 0.5017*** [2.997] 0.2468 [1.452] 75 and older –0.6090*** [–5.560] –0.3392*** [–3.262] 0.5974** [2.387] 0.7027*** [3.020] Years of Education (dummy variables) At least some primary 0.4525*** [4.724] 0.5753*** [8.541] –0.3082* [–1.743] –0.3000** [–2.165] Completed primary school or 0.5900*** [7.949] 0.7014*** [9.822] –0.1542 [–1.152] –0.3948*** [–2.758] more Completed junior high or more 0.5740*** [7.873] 0.5622*** [6.850] –0.5087*** [–3.529] –0.6314*** [–3.623] Education × Age Interaction Years of education × agea –0.0019*** [–3.338] –0.0008 [–1.203] –0.0015 [–1.373] –0.0005 [–0.356] Per Capita Expenditures (splines)b 0 - median pce 0.2001** [2.536] 0.0360 [0.495] –0.2731 [–1.589] –0.2583 [–1.371] > = median pce 0.0135 [0.119] 0.2082** [1.992] –0.0684 [–0.269] 0.0423 [0.162] Constant 0.1745 [0.175] 2.1664** [2.361] 8.3122*** [3.822] 8.2205*** [3.421] Observations 3,748 4,063 3,900 4,399 R-squared 0.283 0.315 0.066 0.068 Province × Rural Yes Yes Yes Yes F-tests for Joint Significance: Age group dummy variables 43.322 0.000 50.164 0.000 7.318 0.000 6.522 0.000 Education variables 104.477 0.000 169.400 0.000 11.726 0.000 21.114 0.000 Educ. years + educ. age 83.455 0.000 137.819 0.000 9.220 0.000 15.859 0.000 interactions Per capita expenditures 13.403 0.000 10.295 0.000 7.236 0.001 2.950 0.053 Province × rural dummy variables 5.347 0.000 5.761 0.000 5.998 0.000 9.180 0.000

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NOTES: The dependent variable for the word recall regression is the average number of the words recalled from the immediate and delayed recalls. Word recall question module was only administered in 2007. The dependent variable for the CES-D regression is the CES-D10 score. The score is computed in the way suggested by the Stanford group that created the CES-D, using numbers from 0 for rarely to 3 for most of the time, for negative questions such as “do you feel sad.” For positive questions, such as “do you feel happy,” the scoring is reversed from 0 for most of the time to 3 for rarely (see text). CESD-10 module was only asked in 2007. t-statistics (in brackets) are based on standard errors that are robust to clustering at the community level. * denotes significant at 10%; ** significant at 5%; *** significant at 1%. The omitted group for age dummy variable is 45 and older, for education, “no schooling,” and for province, Jakarta. aThe interaction term is between years of education and the de-meaned age. Means of age in the word recall sample are 56.9 (male), 56.7 (female); and in the CES-D sample: 57.3 (male), 57.4 (female). bKnot point is at the median pce, coefficient represent change in the slope. SOURCE: Data from IFLS4. 337

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338 AGING IN ASIA CES-D 10 Score As a measure of mental health, the respondents were administered a self-reported depression scale from the short version of the CES-D scale, one of the major international scales of depression used in general popu - lations. Higher scores on the CES-D scale indicate a higher likelihood of having major depression.26 Some recent studies have failed to find a relationship between depression and education or income (see Das et al., 2007, for example); however, other studies have found such correlations. (Patel and Kleinman, 2003, surveyed several studies that do find negative correlations between depression and SES.) For Indonesia, Friedman and Thomas (2008) find that the economic crisis fueled depression indicators, especially for the more vulnerable population.27 Figure 13-5b displays the relationships between CES-D scores and age. For both elderly men and elderly women, CES-D scores increase with age and are higher for women. The mean CES-D scores among people aged 45 and older are 3.3 for men and 3.8 for women. The regressions using CES-D as dependent variable (Table 13-7) show that even in a multivariate setting, age has a strong positive correlation with CES-D scores. The education variables are jointly statistically sig- nificant for both men and women, with the more schooling, the lower the CES-D scores. The age-schooling interactions are not significant. The pce variables, while not individually significant, are jointly significant (at 10% or lower) and show a negative association between pce and CES-D scores.28 CONCLUSIONS Indonesia has undergone major changes in multiple dimensions since the Indonesia Family Life Survey was first fielded in 1993. Among these changes has been moving along the health and nutrition transition. IFLS is very well suited to examine those changes. Overall there have been significant changes in health outcomes among elderly Indonesians over the 15-year period of IFLS. Much of the change 26 The answers for CES-D are on a four-scale metric, from rarely, to some days (1–2 days), to occasionally (3–4 days), to most of the time (5–7 days). We score these answers in the way suggested by the Stanford group that created the CES-D, using numbers from 0 for rarely to 3 for most of the time, for negative questions such as “do you feel sad.” For posi - tive questions, such as “do you feel happy,” the scoring is reversed from 0 for most of the time to 3 for rarely. 27 They also use IFLS data, from 1993 and 2000. Unfortunately the depression scale that IFLS had been using was not as widely used as the CES-D scale, so we switched scales in 2007 to be more comparable to other international surveys, especially the HRS-type surveys. This means that in this chapter, we can only use the CES-D scale for one year, 2007. 28 Similar results are again found in the CHARLS data for China (see Strauss et al., 2011).

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339 FIRMAN WITOELAR, JOHN STRAUSS, and BONDAN SIKOKI can be seen as improvements, such as the movement out of undernutri - tion and communicable disease as well as the increasing levels of hemo- globin, which, however, are still at low levels compared to high-income countries. On the other hand, other changes, such as the increase in over- weight and waist circumference, especially among women, and continu- ing high levels of hypertension and high levels of low HDL cholesterol seem to be inadequately addressed by the health system. These conditions indicate that the elderly population in Indonesia is increasingly exposed to higher risk factors that are correlated with chronic problems such as cardiovascular diseases and diabetes. This is quite interesting because this period has seen major gyrations in economic activity, including strong growth from 1993 to 1996, a major economic collapse from late 1997 to 1998, and a strong recovery from 2000 to 2007. The financial crisis may have slowed the nutrition transition, and some of our evidence is consistent with that conjecture. The relationship between health and SES at different stages in the life cycle is always difficult to disentangle. IFLS enables us to provide some important findings that contribute to our understanding of the relationships. In this chapter we examine correlations between SES and many health outcomes and behaviors for the elderly. Past work has usu - ally been limited to just a small number of health outcomes and has not usually examined the elderly. To the extent that controlling for time, com - munity, and their interactions account for differences in prices, healthcare availability, and quality in the communities over time, the significant correlations that still exist between SES and many of the health outcomes indicate a substantial degree of inequality of health among the elderly population. We find positive correlations between SES and most of the good health outcomes and that education tends to suppress the negative impacts of age on some health outcomes. Of some importance, we find a very large rate of underdiagnosis for hypertension, the one chronic disease of the elderly for which we have data. These rates are differential by SES for women, with lower SES women having a greater chance of being undiagnosed. This is very likely true for other chronic conditions that we could not calculate. This lack of diagnosis indicates that the Indonesian health system, like most others in low-income countries, is still not set up to adequately care for chronic conditions of the elderly. REFERENCES Abikusno, N. (2009). Evaluation and implementation of ageing-related policies in Indonesia. In Older Persons in Southeast Asia: An Emerging Asset, E.N. Arifin and A. Ananta (Eds.). Singapore: ISEAS.

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