Cover Image

PAPERBACK
$69.00



View/Hide Left Panel

3

Longitudinal Aging Study in India: Vision, Design, Implementation, and Preliminary Findings

P. Arokiasamy, David Bloom, Jinkook Lee, Kevin Feeney, and Marija Ozolins

FOUNDATIONS FOR THE LONGITUDINAL AGING STUDY IN INDIA1

The Context: Global Population Aging

Population aging is a global phenomenon that all countries face, but global averages can mask considerable heterogeneity both across and within regions (Bloom, 2011a). Countries are at various stages of the process: The share of the 60+ population ranges from under 5% in a number of African and Gulf countries to more than 20% in several European and East Asian countries.2 However, there is much less heterogeneity with respect to time trends; population aging will take place in all regions and countries going forward.

These trends have given rise to increased public thinking and dialogue on the issue of population aging. Some researchers suggest that population aging has substantial capacity to diminish the productive

____________

1 An early version of this chapter was presented as a paper in March 2011 at the Indian National Science Academy in New Delhi, India, at a conference on “Aging in Asia.” The authors are indebted to the conference participants and to Paul Kowal and Larry Rosenberg for helpful comments. A more detailed analysis of these data is available at http://www.hsph.harvard.edu/pgda/WorkingPapers/2011/PGDA_WP_82.pdf. This research has been supported by NIA Grants R21AG032572 and P30AG024409 and by a grant from the Weatherhead Center for International Affairs at Harvard University.

2 Except where stated otherwise, international demographic data in this report are derived from United Nations (2011).



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 36
3 Longitudinal Aging Study in India: Vision, Design, Implementation, and Preliminary Findings P. Arokiasamy, David Bloom, Jinkook Lee, Kevin Feeney, and Marija Ozolins FOUNDATIONS FOR THE LONGITUDINAL AGING STUDY IN INDIA1 The Context: Global Population Aging P opulation aging is a global phenomenon that all countries face, but global averages can mask considerable heterogeneity both across and within regions (Bloom, 2011a). Countries are at various stages of the process: The share of the 60+ population ranges from under 5% in a number of African and Gulf countries to more than 20% in several European and East Asian countries.2 However, there is much less hetero- geneity with respect to time trends; population aging will take place in all regions and countries going forward. These trends have given rise to increased public thinking and dia - logue on the issue of population aging. Some researchers suggest that population aging has substantial capacity to diminish the productive 1An early version of this chapter was presented as a paper in March 2011 at the Indian National Science Academy in New Delhi, India, at a conference on “Aging in Asia.” The authors are indebted to the conference participants and to Paul Kowal and Larry Rosenberg for helpful comments. A more detailed analysis of these data is available at http://www.hsph. harvard.edu/pgda/WorkingPapers/2011/PGDA_WP_82.pdf. This research has been sup- ported by NIA Grants R21AG032572 and P30AG024409 and by a grant from the Weatherhead Center for International Affairs at Harvard University.. 2 Except where stated otherwise, international demographic data in this report are derived from United Nations (2011). 36

OCR for page 36
37 AROKIASAMY, BLOOM, LEE, FEENEY, and OZOLINS capacities of national economies. Other studies suggest that any negative effects on economic growth are likely to be no more than modest (Bloom, Canning, and Fink, 2010; Boersch-Supan and Ludwig, 2010). Regardless of the effect on the economy as a whole, population aging will lead to increased need for elder care and support, at a time when, in developing societies, traditional family-based care is becoming less the norm than in the past. In addition, a higher share of older people will affect budget expenditures (less for education, but more for healthcare) and may affect tax rates. Population Aging in India: Trends and Challenges With 1.21 billion inhabitants counted in its 2011 census (Registrar General of India, Census of India, 2011), India is the second most popu - lous country in the world. Currently, the 60+ population accounts for 8% of India’s population, translating into roughly 93 million people. By 2050, the share of the 60+ population is projected to climb to 19%, or approxi - mately 323 million people. The elderly dependency ratio (the number of people aged 60 and older per person aged 15 to 59) will rise dramati - cally from 0.12 to 0.31. At the same time, India’s older population will be subject to a higher rate of noncommunicable diseases, a higher share of women in the workforce (thus less able to care for the elderly), children who are less likely to live near their parents, and a lack of policies and institutions to deal effectively with these issues (Bloom, 2011b). 3 Several forces are driving India’s changing age structure, includ- ing an upward trend in life expectancy and falling fertility. An Indian born in 1950 could expect to live for 37 years, whereas today India’s life expectancy at birth has risen to 65 years; by 2050 it is projected to increase to 74 years. Fertility rates in India have declined sharply, from nearly 6 children per woman in 1950 to 2.6 children per woman in 2010. India has also been experiencing a breakdown of the traditional extended family structure; currently, India’s older people are largely cared for privately, but these family networks are coming under stress from a variety of sources (Bloom et al., 2010; Pal, 2007). India is in the early stages of establishing government programs to support its aging population. At the current burden of disease levels, rising numbers of older people will likely increase demands on the health system (Yip and Mahal, 2008). Less than 10% of the population has health insur- ance (either public or private), and roughly 72% of healthcare spending is 3 James (2011) points out that the history of long-term population predictions for India has been marked by major inaccuracies.

OCR for page 36
38 AGING IN ASIA out-of-pocket. The aging population is particularly at risk, as the health insurance scheme for the poor covers only those aged 65 or younger. Older Indians also face economic insecurity; 90% of them have no pension. According to official statistics, labor force participation remains high (39%) among those aged 60 and older and is especially high (45%) in rural areas (see Alam, 2004, and Registrar General, 2001). These high participation rates reflect an overwhelming reliance on the agriculture and informal sectors, which account for more than 90% of all employment in India. They also reflect the inadequacy of existing social safety nets for older people (Bloom et al., 2010). In addition, more than two-thirds of India’s elderly live in rural areas, limiting their access to modern financial institutions and instruments such as banks and insurance schemes. With India in the early stages of a transition to an older society, little is known about the economic, social, and public health implications. Data on the status of older people are needed to analyze population aging and formulate mid- and long-term policies. The Longitudinal Aging Study in India (LASI) is an effort to fill this gap through a large-scale, nation - ally representative, longitudinal survey on aging, health, and retirement. LASI’s longitudinal character is key: Over an extended period, researchers can assemble a data set that shows the changes in India’s older population and, at the same time, have access to up-to-date data. The survey results and subsequent data analyses will be disseminated to the research com- munity and policymakers. LASI joins several existing sister surveys of the seminal Health and Retirement Study (HRS), a longitudinal survey of Americans aged 50 and older conducted by the Institute for Social Research (ISR) at the Uni - versity of Michigan and supported by the National Institute on Aging (NIA). HRS has inspired similar studies outside the United States; cur- rent and planned HRS-type studies cover more than 25 countries on four continents (Lee, 2010). One striking feature of the HRS-type surveys is the possibility of pooling data from different countries to assess the effects of differing institutions on behavior and outcomes. Taken as a whole, the HRS family offers many opportunities to widen and deepen research on the nature and implications of aging.4 4 Another source of valuable micro-data on older populations is the Study on Global AGEing and Adult Health, or SAGE, developed by the World Health Organization Multi- Country Studies Unit. SAGE covers six countries (China, Ghana, India, Mexico, Russian Federation, and South Africa), and while focused on those aged 50+, includes a small sample of adults aged 18-49 years. It has more focus on health and less on economic and financial data than the HRS family of surveys.

OCR for page 36
39 AROKIASAMY, BLOOM, LEE, FEENEY, and OZOLINS LONGITUDINAL AGING STUDY IN INDIA (LASI) Design and Vision In this section of the chapter, we discuss the design and sampling frame for the LASI pilot, highlighting features that allow researchers to begin to identify and answer important questions about population aging in India. We also evaluate the validity of the fieldwork by comparing the LASI pilot sample to that of other surveys in India. India, like other countries in which HRS-style surveys have been conducted, presents a unique set of challenges. Income and assets, for example, are difficult to measure due to lack of written documentation and the fact that a significant portion of income and production does not take place in market contexts. In addition, people may be disinclined to reveal certain information (e.g., some women may be reluctant to reveal that they have savings balances for fear that their husbands or sons-in-law will claim them). To capture India’s demographic, economic, health, and cultural diver- sity, the LASI pilot focused on two northern states (Punjab and Rajasthan) and two southern states (Karnataka and Kerala). Rajasthan and Karnataka provide some overlap with the World Health Organization’s Study on Global AGEing and Adult Health (SAGE). Punjab is an economically developed state, while Rajasthan is relatively poor. Kerala, known for its relatively developed healthcare system, has undergone rapid social devel- opment and is a potential harbinger of how other Indian states might evolve (Pal and Palacios, 2008). The LASI instrument was developed in English and translated into the dominant local languages: Hindi (Punjab and Rajasthan), Kannada (Karnataka), and Malayalam (Kerala). The LASI questionnaire was also designed to collect information con - ceptually comparable to related HRS surveys and SAGE.5 The instrument consists of a household survey, collected once per household by inter- viewing a selected key informant about household finances and living conditions for those in the household; an individual survey, collected for each age-eligible respondent at least 45 years of age and their spouse (regardless of age); and a biomarker module, collected for each consenting age-eligible respondent and spouse. The household interview consists of five sections: a roster detailing basic demographic information about each household member; a ques - 5 These include the Health and Retirement Study (HRS) in the United States, the English Longitudinal Survey of Ageing (ELSA), the Chinese Health and Retirement Longitudinal Survey (CHARLS), the Indonesian Family Life Survey (IFLS), the Korean Longitudinal Study of Ageing (KLoSA), the Japanese Study of Aging and Retirement (JSTAR), and the Study of Health, Ageing and Retirement in Europe (SHARE), which covers 15 European countries.

OCR for page 36
40 AGING IN ASIA tionnaire about the housing and neighborhood environment, including questions about access to water, neighborhood conditions, and other attri- butes; income of all family members from labor and nonlabor sources; assets and debts of the household; and consumption and expenditure of the household on food and nonfood items, including items that were exchanged in-kind, gifted, or home-grown. The individual interview consists of seven sections: demographics, family and social networks, health, healthcare utilization, work and employment, pension and retirement, and one experimental section.6An important component of the health section is a biomarker module collected by the interview team. Given the lack of healthcare services, biological markers (e.g., anthropometrics, blood pressure, and dried blood spots) and performance measures (e.g., gait speed, grip strength, balance, lung function, and vision) allow researchers to assess the health of LASI’s sam- ple population. The dried blood spot collection, for example, allows for up to 35 different assays, including four that the LASI team initially plans to test: C-reactive protein (CRP, a marker of inflammation), glycosylated hemoglobin (HbA1c, a marker of glucose metabolism), hemoglobin (Hb, a marker of anemia), and Epstein-Barr virus (EBV) antibodies (a marker of cell-mediated immune function). Sampling Plan, Fieldwork, and Administration Funded by the National Institute on Aging, LASI is a partnership between the Harvard School of Public Health, the International Institute for Population Sciences in Mumbai, India, and the RAND Corporation. Also involved in LASI are two other Indian institutions, the National AIDS Research Institute (NARI) and the Indian Academy of Geriatrics (IAG). The University of California Los Angeles (UCLA) School of Medi - cine is also a participant in LASI. The fieldwork was carried out by a network of Population Research Centers (see Table 3-1). Fieldwork lasted from October to December 2010. The rapid turnaround from data collection to the analysis of the data was possible through use of state-of-the-art technology in data management and computer-assisted personal interviewing (CAPI). Using the 2001 Indian Census,7 we drew a representative sample from the four states. Age-qualifying individuals were drawn from a stratified, 6 The experimental section consists of a module of questions on one of the following three topics, randomly assigned: economic expectations, anchoring vignettes, and social connectedness. 7 The Indian Census is conducted every 10 years. The 2011 wave was recently released, so the first full LASI wave will be able to utilize the latest population sample during fieldwork.

OCR for page 36
41 AROKIASAMY, BLOOM, LEE, FEENEY, and OZOLINS TABLE 3-1 Administration of the 2010 LASI Pilot Survey Karnataka Kerala Punjab Rajasthan Timeline From 29 October 1 November 14 November 14 November To 3 December 14 December 12 December 18 December Organization Population Population Research Population Population Research Centre, Department Research Research Centre, of Demography, Centre, Centre, Institute for University of Kerala, Department Department Social and Thiruvananthapuram of Economics, of Economics, Economic Himachal University Change, Pradesh of Lucknow, Bangalore University, Lucknow Shimla NOTE: LASI fieldwork was planned in order to avoid monsoon season, which typically lasts from June to September. multistage, area probability sampling design, beginning with census com- munity tracts. From each state, two districts were selected at random from Census districts for 2001; eight primary sampling units (PSUs) were ran- domly selected from each district. PSUs were chosen to match the urban/ rural share of the population. Twenty-five residential households were then selected through systematic random sampling from each PSU, from which an average of 16 households contained at least one age-eligible individual. The LASI pilot achieved a household response rate of 88.5%, calcu - lated as the ratio of consenting to eligible households (as further adjusted for cases of no contact, missing eligibility information, or refusal to give eligibility information; see Table 3-2). The individual response rate (90.9%) and biomarker module response rate (89%) were calculated conditional on belonging to a household that consented to participate in the LASI interview. Eligible households were defined as those with at least one member 45 years of age and older, and eligible individuals were those who were 45 years of age and older or married to an individual who was.8 8Eligible age for response rates was determined from the coverscreen household roster, which was reported by the household respondent, who was not always an individual respondent. The respondent who consented to the individual interview did self-report age in the demographics component of the module, effectively creating two possible age variables. On occasion, some individuals who were listed as 45 years of age and older reported they were not or vice versa in the individual interview. For consistency, we calculate the response rates using ages reported in the coverscreen, though for the remaining analysis presented in the paper we rely on self-reported age. The results of all models were not sensitive to the age variable used.

OCR for page 36
42 TABLE 3-2 LASI Pilot Study, Response Rate Urban Rural Punjab Rajasthan Kerala Karnataka Total Household Survey Sampled 485 1,062 375 371 395 406 1,547 Unable to contact 10 13 0 0 17 6 23 Contact established 475 1049 375 371 378 400 1,524 Age eligible 325 756 254 255 297 275 1,081 Not eligible 140 284 120 114 70 120 424 Unknown eligibility 10 9 1 2 11 5 19 Did not start interview 31 56 28 13 24 22 87 Started interview 294 700 226 242 273 253 994 Completed interview 281 669 222 230 261 237 950 Household Response Rate (%) 85.2 90.0 88.6 94.2 84.0 88.5 88.5 Individual Survey Total eligible 567 1,359 419 485 559 463 1,926 Age eligible 505 1,201 385 423 506 392 1,706 Spouse eligible 62 158 35 61 53 71 220 Started individual interview 492 1,259 410 436 483 422 1,751 Age eligible 439 1,109 375 380 436 357 1,548 Spouse eligible 53 150 35 56 47 65 203 Completed individual interview 472 1,211 402 417 462 402 1,683 Age eligible 419 1,067 368 363 418 337 1,486 Spouse eligible 53 144 34 54 44 65 197 Individual Response Rate (%) 86.8 92.6 97.9 89.9 86.4 91.1 90.9 Biomarker Module Total eligible 567 1359 419 485 559 463 1,926 Consented to start biomarker module 474 1241 398 436 480 401 1,715 Biomarker Response Rate (%) 83.6 91.3 95.0 89.9 85.9 86.6 89.0

OCR for page 36
Response Rates for Selected Questions (%) Dried blood spot collection (biomarker 64.6 77.5 76.4 75.8 69.6 76.5 74.3 module) Satisfaction with spousal relationship 93.4 93.9 97.3 90.6 89.0 99.7 93.8 (family and social networks) Income (household questionnaire) 77.2 79.2 77.9 73.5 85.1 77.2 78.6 Consumption (household questionnaire) 79.4 82.8 92.3 73.9 80.8 80.6 81.8 “Probability” respondent will die in 87.3 88.0 94.7 89.1 71.6 98.5 87.8 one year (expectations module) NOTES: Response rates are calculated by dividing the total number of individuals or households who consented to the interview by the total number of contacted, eligible individuals (including spouses under 45 years of age) or households as reported in the coverscreen household com - ponent of the interview. Households that were not contacted indicate cases when the interviewing team was unable to speak with an individual residing at the house either because no one was home, the family has moved, or for some other reason. Five contact attempts were suggested before classifying a household as “no contact.” The household response rate across all states is thus calculated by dividing 994 households that initially consented by the sum of the number of no contacts (23), the contacted eligible households (1,081), and the 19 households with missing or refused age eligibility. Note that this reflects a conservative estimate to the response rate. The individual response rate across all states is calculated by dividing the 1,751 individuals who consented to start the individual interview by the 1,926 eligible household members listed in the coverscreen of the household roster once the survey began. Response rates for select questions pertain to respondents who were asked that specific question, not the total eligible persons listed in the coverscreen. This approach was chosen to best capture the effects of the sensitive nature of the questions. Thus the dried blood spot collection response rate captures the share of respondents who specifically agreed to participate. Response rates for income and consumption are among households and are the share of households that did not require imputation and had no missing income components queried about during the household module. The probability respondents will die in one year is a question from the expectations module, one of three experimental modules that was randomly assigned to respondents at the end of the individual interview. The question asked respondents to select a number of beans from a pile of 10 beans to indicate how likely they were to die in the next year. Response rates for more standard survey questions were 98% and above; response rates of selected questions were chosen to showcase survey items with lower response rates. SOURCE: Data from Longitudinal Aging Study in India (LASI) Pilot Wave. 43

OCR for page 36
44 AGING IN ASIA Among households and individuals who consented to start the LASI interview, not all individual or household modules were completed after initial consent was given. Table 3-2 tabulates the number of respondents and households that completed an individual or household interview; these 950 households and 1,683 individuals constitute the complete LASI pilot sample. Of the 1,683 individuals who completed an individual inter- view, 1,486 respondents9 were aged 45 years and older. The 197 who were not age-eligible were female spouses of age-qualifying participants. Table 3-2 also shows relatively high response rates to selected poten- tially sensitive questions. We observed significant heterogeneity in the length of time to com- plete the survey across states, from a total time of 137 minutes in Rajasthan and Karnataka to a high of 215 minutes in Punjab (see Table 3-3). Some interviews were split over time: about 15% of the interviews occurred over a span of two or more days.10 The average duration of the household module was 33 minutes. For the individual interview, including the biomarker module, the mean duration was 78 minutes. Households had a mean of 1.8 respondents who completed individual interviews. Profile of LASI Respondents The LASI design and implementation was successful in creating a sample comparable to other nationally representative surveys conducted in India. In Table 3-4, we present the initial results of the fieldwork through a comparison of the basic demographic indicators of LASI respondents to those of respondents from other surveys conducted in India: the National Sample Survey (NSS), India Human Development Survey (IHDS), World Health Survey (WHS), and SAGE. As the other surveys have broader age inclusion categories, we restrict the comparison to individuals aged 45 and older only. We compare the distribution of demographic characteristics for those aged 45 and older across the four surveys, looking specifically at age, sex, urban-rural residence, marital status, and education. We expect some dif - ferences across these metrics, given the different sets of states surveyed. For example, LASI has a comparatively small sample size from four 9 Of the 1,486 respondents who were identified in the coverscreen as being aged 45 and older, 1,451 confirmed that status in the individual interview. We use these 1,451 as our analysis sample. The remaining 232 respondents consists of 230 who self-reported their age as less than 45 (of which 181 were also identified as less than age 45 in the coverscreen), and 2 who did not report an age. These 232 individuals were not included in the analysis sample. 10 Such a span took place when at some point during the interview, the interview team was asked to leave and come back on a different day.

OCR for page 36
45 AROKIASAMY, BLOOM, LEE, FEENEY, and OZOLINS TABLE 3-3 Average Survey Duration by State of Key Survey Components (in minutes) All Punjab Rajasthan Kerala Karnataka States Total Time at Household (HH) 215.2 137.3 205.2 137.4 174.7 Household Module Total 41.9 29.1 37.6 24.6 33.4 Housing and environment 9.2 7.6 7.0 5.3 7.2 Consumption 12.3 7.6 13.1 7.3 10.1 Income 7.9 5.0 7.4 5.2 6.4 Agricultural income and assets 3.9 4.0 1.7 2.1 2.9 Financial assets and real estate 8.6 5.0 8.5 4.8 6.8 Number of interviews 222 230 261 237 950 Individual Module Total 93.9 57.8 92.5 66.5 78.1 Demographics 9.5 5.7 6.7 5.6 6.9 Family and social network 15.0 11.2 13.1 8.9 12.1 Health 27.4 17.2 30.5 15.9 23.0 Healthcare utilization 4.7 2.7 4.8 3.0 3.8 Employment 7.2 2.5 7.3 4.8 5.5 Pension 4.2 1.1 2.7 2.0 2.5 Experimental: social 11.3 4.9 10.0 7.6 8.4 connectedness Experimental: expectations 5.9 3.5 6.2 3.0 4.7 Experimental: vignettes 4.8 1.5 4.1 1.7 3.1 Biomarker 18.9 14.2 21.0 22.3 19.1 Number of interviews 402 417 462 402 1,683 Number of individual interviews 1.8 1.8 1.8 1.7 1.8 per HH Duration of Interviews One day (n) 298 338 390 380 1,406 Multiple days (n) 103 74 61 20 258 Interviews lasting multiple 25.7 18.0 13.5 5.0 15.5 days (%) NOTE: Total time at HH is the average time spent at a household, including the time spent conducting the household module and all individual modules (including the biomarker module). SOURCE: Data from Longitudinal Aging Study in India (LASI) Pilot Wave.

OCR for page 36
46 TABLE 3-4 External Validity: Comparison of LASI to Other Surveys on Select Demographic Indicators All States in Sample Rajasthan Karnataka LASI NSS IHDS WHS SAGE LASI SAGE LASI SAGE Survey year(s) 2010 2004 2004–05 2003 2007–08 2010 2007–08 2010 2007–08 Total number of individuals 1,683 383,338 215,754 10,750 12,198 417 2,374 402 1,744 Number of individuals aged 45+ 1,451 81,146 45,074 3,706 7,841 358 1,587 315 1,139 Age structure (%) Among Respondents 45 Years and Older Age 45–54 44.3 44.1 44.9 41.7 48.7 43.1 49.9 49.5 52.3 Age 55–64 28.4 32.7 29.7 26.1 28.3 23.4 26.9 31.8 25.8 Age 65–74 17.8 17.4 17.9 18.1 16.4 21.8 16.3 14.0 15.2 Age 75+ 9.5 5.9 7.6 14.1 6.7 11.8 6.8 4.8 6.8 Sex (%) Among Respondents 45 Years and Older Male 48.7 50.5 51.4 50.7 55.2 51.5 53.7 47.6 56.6 Female 51.3 49.5 48.6 49.4 44.8 48.5 46.3 52.4 43.5 Residence (%) Among Respondents 45 Years and Older Urban 27.1 26.3 26.9 11.1 26.8 19.2 20.5 35.7 32.3 Rural 72.9 73.8 73.1 88.9 73.2 80.8 79.5 64.3 67.7 Marital Status (%) Among Respondents 45 Years and Older Married 78.0 75.8 78.2 80.7 81.5 81.0 81.5 75.3 82.4 Never married 1.8 1.1 0.7 1.3 0.6 0.9 0.3 2.2 0.4 Divorced 1.2 0.6 0.5 0.7 0.6 1.4 0.7 0.6 0.5 Widowed 19.1 22.5 20.6 17.3 17.3 16.8 17.5 21.9 16.7

OCR for page 36
64 AGING IN ASIA were less likely to participate in social activities compared with those in other states. These models provide some evidence that aging Indians continue to stay involved in their communities as they age. They stop working for pay, are active outside the home, and participate in broader civic and social networks. The LASI pilot suggests research in civic and social net - works in India is promising. Previous studies have supported the impor- tance of civic and social participation for successful aging and health, and we see some evidence of that with the connection between difficulty with activities of daily life and social participation (Berkman et al., 2000; Moen, Dempster-McClain, and Williams, 1992; Seeman and Crimmins, 2006). Economic Well-Being of the Aging LASI provides considerable information about the economic activ- ity and well-being of India’s aging population. Workforce participation, for example, is central in a country without social security or pensions, particularly as intergenerational support—once the traditional and wide - spread means of old age support—becomes less common (Bloom et al., 2010). Given that less than 11% of older people in India have access to a pension or social security, economic activity is especially important. Addi- tionally, private saving is often difficult or entirely infeasible for several reasons: earnings are low, a significant portion of the economic activity is informal and may not be tied to cash exchange, and bank accounts are often not available in rural India (Uppal and Sarma, 2007). We examine labor force participation (defined as any employment, self-employment, or agricultural work in the past 12 months) in the LASI sample among respondents who are aged 45 and older. Table 3-10 presents five models of labor force participation. The results show older respondents are less likely to work, particularly in urban areas. Women are less likely to report having worked, as are respondents who report difficulty or disability with at least one ADL. The association between disability and economic activity points to the important relationship between health and economic well-being among the vulnerable and aging Indian population, although one cannot infer the direction of cau - sality. These findings are consistent with results of similar studies (Bakshi and Pathak, 2010). Studies from other developing countries, such as China, have found health is a significant correlate of labor market partici- pation among socioeconomically disadvantaged populations (Benjamin, Brandt, and Fan, 2003). We do not see employment differences by education or caste. Respon- dents in Rajasthan are more likely to be working than respondents in other states. This finding is consistent with the largely agricultural economies in

OCR for page 36
65 AROKIASAMY, BLOOM, LEE, FEENEY, and OZOLINS Rajasthan and other rural areas, which absorb older workers more consis- tently in comparison with manufacturing and other types of economies in developing countries (Bakshi and Pathak, 2010; Nasir and Ali, 2000). Education is not correlated with labor force participation among our sample aged 45 years and older, with the exception of the model for non - agricultural labor. Consistent with the literature, respondents with more education are slightly more likely to engage in nonagricultural labor than those with less, even after controlling for a variety of socioeconomic and regional indicators. However, our findings are somewhat inconsistent with results elsewhere that suggest educated individuals are more likely to accumulate savings and participate in formal labor markets, leading to earlier labor-market withdrawal. Our estimates reveal insignificant associ- ations with education and all other forms of work across rural and urban sectors. However, the model could mask regional heterogeneity: When we estimate the models without state dummies, we find statistically signifi - cant relationships between education and labor force participation in non- agricultural sectors, work in rural areas (model 2), and agricultural work (model 4). In these three models without state dummies, more educated individuals were less likely to be working. Regional differences in avail- ability of pension schemes, old age support, and labor markets account for the association between education and labor force participation in our sample. Given the lack of social security, pension, and health insur- ance available to most Indians, continued workforce participation is vital. However, working imposes a strain on aging individuals, and many often do so out of desperation or necessity. Policies can focus on the health of the aging workforce, so they may stay engaged in more productive work. We also examine household expenditure. Among households, we analyze the demographic and regional correlates of household consump - tion expenditure to understand socioeconomic gradients in the LASI sam- ple and to some extent in India as well. Figure 3-4 displays the distribu - tion of annual household expenditure (in rupees, and including imputed amounts) per equivalent adult across LASI respondents aged 45 and older. Equivalency scales developed by the Organisation for Economic Co-operation and Development (OECD) are used to account for econo- mies of scale in household consumption.19 Table 3-11 reports three regression models of household expenditure per equivalent adult among LASI respondents. Large households tend to have lower per capita expenditure. Across the pooled urban and rural sam- 19Equivalent adults are calculated counting the first person aged 18 and older as 1.0 equiva- lent adults, each additional person aged 18 and older as 0.7 equivalent adults, and each person under age 18 as 0.5 equivalent adults. See http://www.oecd.org/dataoecd/61/52/35411111. pdf.

OCR for page 36
66 TABLE 3-10 Demographic and Regional Variation in Binary Indicator for Any Employment in Past Year All Work, Agricultural Nonagricultural Work, Rural and Urban All Work, All Work, Work, Rural Urban, and Rural Respondents Rural Respondents Urban Respondents Respondents Respondents Aged 55–64 –0.417*** –0.374** –0.522* –0.200 –0.357** (–4.23) (–3.42) (–2.33) (–1.94) (–3.29) Aged 65–74 –0.689*** –0.612*** –0.966** –0.317* –0.542** (–5.69) (–4.33) (–4.31) (–2.39) (–3.37) Aged 75+ –1.227*** –1.103*** –1.889*** –0.737*** –0.926*** (–6.47) (–5.31) (–4.42) (–3.62) (–4.26) Female –1.349*** –1.282*** –1.597*** –0.858*** –1.016*** (–14.27) (–14.26) (–5.66) (–9.89) (–7.91) Education (yrs) –0.007 0.002 –0.022 –0.022 0.029* (–0.57) (0.14) (–1.03) (–1.98) (2.45) Rajasthan 0.427** 0.389* 0.604** 0.531** –0.070 (2.80) (2.16) (3.34) (2.88) (–0.47) Kerala –0.086 –0.286 0.341 –0.432* 0.056 (–0.53) (–1.59) (1.54) (–2.10) (0.44) Karnataka 0.300 0.355 0.270 0.528** –0.047 (1.89) (1.88) (1.52) (2.97) (–0.36) Rural 0.312** –0.488*** (2.97) (–5.41) Scheduled caste 0.211 0.108 0.473 –0.256 0.483* (1.53) (0.69) (1.88) (–1.69) (3.44) Scheduled tribe 0.093 0.071 –0.041 0.040 –0.199 (0.51) (0.38) (–0.09) (0.26) (–0.71) Other backward caste 0.093 0.038 0.243 –0.101 0.175 (0.98) (0.33) (2.13) (–0.94) (1.67) ADL disability count –0.150** –0.112* –0.263* –0.107* –0.089 (–3.02) (–2.06) (–2.67) (–2.12) (–1.39)

OCR for page 36
Constant 0.478* 0.772** 0.582 0.108 –0.124 (2.62) (3.33) (2.15) (0.54) (–0.85) N 1,428 1,023 405 1,023 1,428 F-stat 20.74*** 19.84*** 14.01 19.56*** 11.07*** Estimator probit probit probit probit probit NOTES: Labor force participation is a dummy variable for having worked in the past 12 months. It includes self-employment, employment by another, or agricultural work both paid and unpaid as reported in the household income module by a household financial respondent or self- reported in the individual interview. The sample is restricted to respondents who self-reported an age of at least 45 years old; LASI used a stratified sampling design, which sampled respondents independently by state, rural-urban area, and district. All multivariate models are unweighted, and the standard errors have been corrected for design effects of stratification. 44.6% of respondents were classified as working. ADL disability count is the number of activities of daily life the respondent has some difficulty with or cannot do. Table presents coefficients with t statistics in parentheses:, * denotes p < 0.05; ** p < 0.01; *** p < 0.001. SOURCE: Data from Longitudinal Aging Study in India (LASI) Pilot Wave. 67

OCR for page 36
68 AGING IN ASIA 20 Percentage of Respondents 15 10 5 0 0 100,000 200,000 300,000 Distribution of per Equivalent Adult Expenditure Across LASI Respondents FIGURE 3-4 Distribution of annual household expenditure per equivalent adult for age-eligible LASI respondents. NOTES: The mean annual per equivalent adult expenditure taken across respon - Figure 3-4 dents (aged 45+) is 54,986 rupees, and the median is 41,993 rupees. The 3% of respondents with more than 300,000 Rs per capita were excluded from this graph. SOURCE: Data from Longitudinal Aging Study in India (LASI) Pilot Wave. ple, scheduled castes, especially those in rural areas, have lower per capita consumption, reflecting in part the geographic isolation of rural com- munities. Other affiliations are also significant: scheduled tribes in urban areas also have statistically lower per capita consumption. This reflects a continued disadvantage for these groups despite many initiatives by the Indian government to improve their well-being (Subramanian et al., 2008). Table 3-11 also reflects geographic differences in households’ per capita consumption. Households in Rajasthan have lower expenditures, especially in rural areas. To account for the effects of household compo- sition by gender and age, we examine the percentage of women in the household and both the youth and elderly dependency ratio. The youth dependency ratio is the number of respondents under 15 years of age divided by the number of respondents of working age, which we define as ages 15 to 59. Our results show that a higher youth dependency ratio lowers per capita expenditure, presumably reflecting standard life cycle patterns of earnings and expenditure (Bloom et al., 2011). Interestingly, we do not see significant effects of the percentage of women or older people on expenditure. This finding is somewhat puzzling

OCR for page 36
69 AROKIASAMY, BLOOM, LEE, FEENEY, and OZOLINS TABLE 3-11 Demographic and Regional Variation in Household Expenditure per Equivalent Adult Household Characteristics All Urban Rural Rural HH –0.017 (–0.19) HH size –0.026* –0.061 –0.012 (–2.62) (–1.86) (–1.15) Scheduled caste –0.449*** –0.334* –0.488*** (–4.90) (–2.67) (–4.31) Scheduled tribe –0.352 –0.889** –0.331 (–1.98) (–3.25) (–1.72) Other backward caste –0.123 –0.103 –0.151 (–1.30) (–0.91) (–1.30) Rajasthan –0.261 –0.167 –0.315 (–1.90) (–1.16) (–1.79) Kerala –0.112 –0.023 –0.128 (–1.04) (–0.13) (–0.99) Karnataka 0.299* 0.514 0.207 (2.28) (1.97) (1.48) Youth dependency ratio –0.167* –0.180 –0.171* (–2.49) (–0.93) (–2.51) Elderly dependency ratio –0.017 –0.107 –0.006 (–0.31) (–0.65) (–0.11) Percentage of women in HH –0.315 –0.713* –0.164 (–1.98) (–2.21) (–1.09) Constant 11.31*** –0.167 11.19*** (75.51) (–1.16) (71.08) N 730 207 523 R sq. 0.1681 0.2300 0.1536 F-stat 6.61*** 3.81 5.78*** Estimator OLS OLS OLS NOTES: Dependent variable is log of household expenditure per equivalent adult household member. The unit of observation is the household, not the individual in these models. The youth dependency ratio is the number of household members 0 to 14 years of age divided by the number of household members 15 to 59 years of age. The elderly dependency ratio is the number of household members aged 60 years and older divided by the number of house- hold members 15 to 59 years of age. The models also exclude households where expenditure was imputed. LASI used a stratified sampling design that sampled respondents indepen - dently by state, rural-urban area, and district. All multivariate models are unweighted and the standard errors have been corrected for design effects of stratification. Standard errors are corrected for design effects and stratified on state, urban/rural residence, and district. Caste is the caste of the head of the household. Table presents coefficients with t statistics in parentheses. * denotes p < 0.05; ** p < 0.01; *** p < 0.001. SOURCE: Data from Longitudinal Aging Study in India (LASI) Pilot Wave.

OCR for page 36
70 AGING IN ASIA given the relatively low labor force participation rates of women and older household members. These two results suggest that older household members, as well as women, are contributing to the household economy in other ways not measured by labor force participation, or cash inflows. This may be especially true for rural households where much of the work is agricultural and subsistence-based, and women and older people may be contributing mostly undocumented household labor. Indeed, in urban areas where this type of household work is less common, we see that the percentage of women in the household is significant and negative, reflect- ing their lower earnings (either cash or in-kind). CONCLUSION LASI is well positioned to play a critical role in the conduct of rigor- ous policy-relevant research as India continues substantial transitions in the demographic, economic, and epidemiologic domains. The fact that the LASI pilot achieved high response rates and that respondent demographics are similar to those of other nationally representative surveys within India lends credibility to the survey’s results concerning the well-being of aging Indians. This chapter highlights the wide geographic variability in health, social, and economic markers across India. Even after adjusting for demo- graphic differences, we still observe state-level variation across all three domains. Capturing the regional heterogeneity is critical for designing effective policy, and the main wave of LASI will expand on this by sam - pling 15 or more states and possibly two union territories as well. Our analysis focuses on the well-being and economic status of aging Indians. While the country seeks to develop economically, basic living conditions and emerging health concerns are major problems. Our analy - sis (herein and in the earlier online version) reveals socioeconomic gra- dients across a variety of health domains, including both subjective and objective measures of self-rated health, disability, and cognitive function - ing. With little institutional support, the aging population’s economic activity is of particular importance given the relative absence of social security and health insurance. Our findings show that aging family mem- bers continue to be contributing members of the household economy. Improving the health of aging Indians could foster higher labor force participation as well. The social and civic lives of older Indians are also key to understanding their contributions to communities. We have found that even the oldest individuals remain socially engaged and that aging women, especially, continue to contribute to civic life in their community. Early results from LASI suggest that older Indians are subject to a wide-ranging set of health, social, and financial insecurities, with a

OCR for page 36
71 AROKIASAMY, BLOOM, LEE, FEENEY, and OZOLINS good deal of variation in myriad dimensions. Conduct of blood assays, expansion of the LASI sample, and collection of longitudinal data are the planned next steps in this effort. Such an evidence base should provide researchers with the raw material they need to better understand aging in India and to design policies that will improve the experience. REFERENCES Ahmed, A.U., H.A. Khan, and R.K. Sampath. (1991). Poverty in Bangladesh: Measurement, decomposition and intertemporal comparison. Journal of Development Studies 27(4):48-63. Alam, M. (2004). Ageing, old age income security and reforms: An exploration of Indian situation. Economic and Political Weekly 39(33):731-740. Alwan, A., D.R. MacLean, L.M. Riley, E.T. d’Espaignet, D. Mathers, G.A. Stevens, and D. Bettcher. (2010). Monitoring the surveillance of chronic non-communicable disease: Progress and capacity in high-burden countries. The Lancet 376:1,861-1,868. Bakshi, S., and P. Pathak. (2010). Who Works at Older Ages? The Correlates of Economic Activity and Temporal Changes in Their Effects: Evidence from India. Working paper, Indian Statisti- cal Institute, Kolkata. Benjamin, D., L. Brandt, and J.Z. Fan. (2003). Ceaseless Toil? Health and Labour Supply of the Elderly in Rural China. University of Toronto manuscript. Available: http://www. princeton.edu/rpds/papers/pdfs/benjamin_ceaseless_toil.pdf. Berkman L., T. Glass, I. Brissette, and T. Seeman. (2000). From social integration to health: Durkheim in the new millennium. Social Science and Medicine 51:843-857. Bloom, D.E. (2005). Education and public health: Mutual challenges worldwide: Guest edi - tor’s overview. Comparative Education Review 49(4):437-451. Bloom, D.E. (2011a). 7 billion and counting. Science 33:562-569. Bloom, D.E. (2011b). India’s baby boomers: Dividend or disaster? Current History (April):143-149. Bloom, D.E., D. Canning, and G. Fink. (2010). Implications of population aging for economic growth. Oxford Review of Economic Policy 26(4):583-612. Bloom, D.E., D. Mahal, L. Rosenberg, and J. Sevilla. (2010). Economic security arrangements in the context of population aging in India. International Social Security Review 63:3-4. Bloom, D.E., D. Canning, G. Fink, and J. Finlay. (2011). Micro Foundations of the Demographic Dividend. Paper presented at the International Union for the Scientific Study of Popula- tion Seminar on Demographics and Macroeconomic Performance, June 2010. Revision presented at the 2011 Annual Meetings of the Population Association of America. Boersch-Supan, A., and A. Ludwig. (2010). Old Europe is aging: Reforms and reform back - lashes. Pp. 169-204 in Demography and the Economy, J. Shoven (Ed.). Chicago: University of Chicago Press. Chaudhuri, A. (2009). Spillover impacts of a reproductive health program on elderly women in rural Bangladesh. Journal of Family Economic Issues 30:113-125. Chen, B., and A. Mahal. (2010). Measuring the health of the Indian elderly: Evidence from National Sample Survey data. Population Health Metrics 8:30. Clark, T.A., and D. Ning. (2007). Towards a spatially disaggregated material-based hard - ship index for the cities of developing nations. International Development Planning and Review 29(10):69-92. Dandekar, K. (1996). The Elderly in India. New Delhi: SAGE. Delevande, A., X. Gine, and D. McKenzie. (2010). Measuring subjective expectations in developing countries: A critical review and new evidence. Journal of Development Eco- nomics 94:151-163.

OCR for page 36
72 AGING IN ASIA Ferro-Luzzi, A., S. Sette, M. Franklin, and W.P.T. James. (2009). A simplified approach of assessing adult chronic energy deficiency. European Journal of Clinical Nutrition 46:173-186. Ganguli, M., G. Ratcliff, V. Chandra, S.D. Sharma, S. Gilby, R. Pandav, S. Belle, C. Ryan, C. Baker, E. Seaberg, and S. Dekosky. (1996). A Hindi version of the MMSE: The develop - ment of a cognitive screening instrument for a largely illiterate rural population in India. International Journal of Geriatric Psychiatry 10:367-377. Gol-Propoczyk, H. (2010). Age, Sex, and Race Effects in Anchoring Vignette Studies: Method- ological and Empirical Contributions. Center for Demography and Ecology, CDE working paper #2010-18, University of Wisconsin–Madison. Available: http://www.ssc.wisc. edu/cde/cdewp/2010-18.pdf. Gupta, I., and P. Dasgupta. (2003). Health-seeking behavior in urban Delhi: An explor- atory study. World Health and Population 3(2). Available: http://www.longwoods.com/ publications/world-health-population/388. Hopkins, D.I., and G. King. (2010). Improving anchoring vignettes: Designing surveys to correct for interpersonal incomparability. Public Opinion Quarterly 74(2):201-222. Husain, Z., and S. Ghosh. (2011). Is health status of elderly worsening in India? A com - parison of successive rounds of the National Sample Survey data. Journal of Biosocial Sciences 41(4):457-467. Indian Human Development Survey. (2005). Home page. University of Maryland and National Council of Applied Economic Research, New Delhi. Available: http://ihds. umd.edu/. International Institute for Population Sciences and Macro International. (2007). National Family Health Survey (NFHS-3), 2005-2006: India. Available: http://www.nfhsindia.org. James, K.S. (2011). India’s demographic change: Opportunities and challenges. Science 333(6,042):576-580. Jotheeswaran, A.T., J.D. Williams, and M.J. Prince. (2010). The predictive validity of the 10/66 dementia diagnosis in Chennai, India: A three-year follow up study of cases identifiable at baseline. Alzheimer Disease and Associated Disorders 24(3):296-302. Kowal, P., K. Kahn, N. Ng, N. Naidoo, S. Abdullah, A. Bawah, F. Binka, N. Chuc, C. Debpuur, A. Ezeh, F.X. Gomez-Olive, M. Hakimi, S. Hirve, A. Hodgson, S. Juvekar, C. Kyobutungi, J. Menken, H.V. Minh, O. Sankoh, K. Streatfield, S. Wall, S. Wliopo, P. Byass, S. Chatterji, and S.M. Tollman. (2010). Ageing and adult health status in eight lower-income countries: The in-depth WHO-SAGE collaboration. Global Health Action Supplement, World Health Organization 3(2):11-22. Kumar, B.G. (1993). Quality of life and morbidity: A reconstruction of some of the paradoxes from Kerala, India. Population and Development Review 19(1):103-121. Lancasr, E., and J. Louviere. (2006). Deleting irrational response from discrete choice experi - ments: A case of investigating or imposing preferences? Health Economics 15:797-811. Lang, I.A., D.J. Llewellyn, K.M. Langa, R.B. Wallace, F.A. Huppert, and D. Melzer. (2008). Neighborhood deprivation, individual socioeconomic status, and cognitive functioning in older people: Analyses from the English Longitudinal Study of Aging. Journal of the American Geriatric Society 56:191-198. Langa, K.M., E.B. Larson, J.H. Karlawish, D.M. Cutler, M.U. Kabeto, S.Y. Kim, and A.B. Rosen. (2008). Trends in the prevalence and mortality of cognitive impairment in the United States: Is there evidence of a compression of cognitive morbidity? Alzheimer’s & Dementia 4:134-144. Lee, J. (2010). Data sets on pensions and health: Data collection and sharing for policy design. International Social Security Review 63(3-4):197-222. Lee, J., R. Shih, K. Feeney, and K. Langa. (2011). Cognitive Health of Older Indians: Individual and Geographic Determinants of Female Disadvantage. Working paper #WR-889. Santa Monica, CA: RAND.

OCR for page 36
73 AROKIASAMY, BLOOM, LEE, FEENEY, and OZOLINS Lee, J., P. Arokiasamy, A. Chandra, P. Hu, J. Liu, and K. Feeney. (2012). Markers and driv - ers: Cardiovascular health of middle-aged and older Indians. Chapter 16 in Aging in Asia: Findings from New and Emerging Data Initiatives. J.P. Smith and M. Majmundar, Eds. Panel on Policy Research and Data Needs to Meet the Challenge of Aging in Asia. Committee on Population, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press. Longitudinal Aging Study in India, Pilot Wave. (2011). Harvard School of Public Health, International Institute of Population Sciences, Mumbai, India, and RAND Corporation. Available: https://mmicdata.rand.org/megametadata/?section=study&studyid=36. Mahal, A., A. Karan, and M. Engelgau. (2010). The Economic Implications of Non-Communicable Disease for India. Washington, DC: World Bank. Mangham, L.J., K. Hanson, and B. McPake. (2009). How to do (or not to do)…Designing a discrete choice experiment for application in a low income country. Health Policy and Planning 24:151-158. Mathuranath, P.S., P.J. Cherian, R. Mather, S. Kumar, A. George, A. Alexander, N. Ranjith, and P.S. Sharma. (2009). Dementia in Kerala, South India: Prevalence and influence of age, education and gender. International Journal of Geriatric Psychiatry 25:290-297. Ministry of Statistics and Programme Implementation. (2004). National Sample Survey. Government of India. Available: http://mospi.nic.in/mospi_new/site/inner. aspx?status=4&menu_id=87. Moen, P., D. Dempster-McClain, and R.M. Williams. (1992). Successful aging: A life-course perspective on women’s multiple roles and health. American Journal of Sociology 97:1,612-1,638. Nasir, Z.M., and S.M. Ali. (2000). Labour market participation of the elderly. The Pakistan Development Review 39(4):1,075-1,086. Nube, M., W.K. Asenso-Okyere, and G.J.M. van den Boom. (1998). Body mass index as indicator of standard of living in developing countries. European Journal of Clinical Nutrition 52:136-144. Pal, S. (2007). Intergenerational Transfers and Elderly Coresidence with Adult Children in Rural India. IZA discussion paper #2847, University of Bonn, Germany. Pal, S., and R. Palacios. (2008). Understanding Poverty among the Elderly in India; Implications for Social Pension Policy. IZA discussion paper #3431, University of Bonn, Germany. Prince, M. (1997). The need for research on dementia in developing countries. Tropical Medi- cine and International Health 2(10):993-1,000. Rajan, S.I., and K.S. James. (1993). Kerala’s health status: Some issues. Economic and Political Weekly 28(36):1,889-1,892. Registrar General, Census of India. (2001). Socioeconomic Tables. New Delhi: Office of the Registrar General, Government of India. Registrar General of India, Census of India. (2011). Census of India. New Delhi: Office of the Registrar General, Ministry of Home Affairs, Government of India. Seeman, T., and E. Crimmins. (2006). Social environment effects on aging and healt h. Annals of the New York Academy of Sciences 954:88-117. Sen, A. (2002). Health: Perception versus observation. British Medical Journal 324(7,342):860-861. Sengupta, M., and E. Agree. (2003). Gender, health, marriage and mobility difficulty among older adults in India. Asia-Pacific Population Journal 18(4):53-65. Subramanian, S.V., L.K. Ackerson, M.A. Subramanyam, and K. Sivaramakrishnan. (2008). Health inequalities in India: The axes of stratification. The Brown Journal of World Affairs 14(2):127-138. Subramanian, S.V., M.A. Subramanyam, S. Selvaraj, and I. Kawachi. (2009). Are self-reports of health and morbidities in developing countries misleading? Evidence from India. Social Science & Medicine 68:260-265.

OCR for page 36
74 AGING IN ASIA Suryanarayana, M.H. (2008). Morbidity Profiles of Kerala and All-India: An Economic Perspective. Working paper #2008-007. Mumbia: Indira Gandhi Institute of Development Research, Mumbai. Available: http://www.igidr.ac.in/pdf/publication/WP-2008-007.pdf. United Nations. (2011). World Population Prospects: The 2010 Revision. New York: United Nations Population Division. Uppal, S., and S. Sarma. (2007). Aging, health and labour market activity: The case of India. Journal of World Health and Population 9(4):79-97. World Bank. (2002). India: Household Energy, Indoor Air Pollution, and Health. ESMAP/South Asia Environment and Social Development Unit, November. Washington, DC: World Bank. World Health Organization. (2003). World Health Survey. Available: http://www.who.int/ healthinfo/survey/en/. World Health Organization. (2004). WHO Medicines Strategy: Countries at the Core, 2004-2007. Available: http://apps.who.int/medicinedocs/pdf/s5571e/s5571e.pdf. World Health Organization. (2010). Study on Global AGEing and Adult Health, Wave 1. Avail- able: http://www.who.int/healthinfo/systems/sage/en/index1.html. Yip W., and A. Mahal. (2008). The health care systems of China and India: Performance and future challenges. Health Affairs 27(4). Zunzunegui, M.V., B.E. Alvarado, F. Beland, and B. Vissandjee. (2008). Explaining health differences between men and women in later life: A cross-city comparison in Latin America and the Caribbean. Social Science and Medicine 68:235-242.