Quality of life among older adults in China and India: Does productive engagement help?

Accepted Manuscript Quality of life among older adults in China and India: Does productive engagement help? Shu Hu, Dhiman Das PII:

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10.1016/j.socscimed.2018.06.028

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Accepted Date: 23 June 2018

Please cite this article as: Hu, S., Das, D., Quality of life among older adults in China and India: Does productive engagement help?, Social Science & Medicine (2018), doi: 10.1016/ j.socscimed.2018.06.028. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Quality of Life among Older Adults in China and India: Does Productive Engagement help? a*

Shu HU

Dhiman DAS

b

*

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Corresponding author

a

Asia Research Institute and Centre for Family and Population Research, National University of Singapore Address: AS8 Level 7, 10 Kent Ridge Crescent, Singapore 119260

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Tel: (65) 6516 4547 Fax: (65) 6779 1428

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Email: [email protected]

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Quality of Life among Older Adults in China and India: Does Productive Engagement Help?

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ABSTRACT

Individuals in developing countries often engage in paid and unpaid work till late in life due to low household savings and limited welfare provisions. Yet, physical disabilities associated with

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aging can limit their ability to work. While work can be beneficial for economic and

psychological well-being, this paper investigates whether engagement in paid and unpaid work

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mediates the impact of physical disabilities on quality of life for older adults. We exploit the different levels of health services and social security in rural and urban China and India to examine the effect of public provisions in the process. We use nationally representative data of individuals aged 50 and above from the World Health Organization Study on Global Ageing and

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Adult Health Wave 1, conducted in 2008-10 in China and in 2007-08 in India. Using a causal mediation analysis framework, we find that paid work plays a minor role in mediating the effect of physical disabilities on quality of life in all societies, and the mediated effect is smaller in

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urban China than in other societies. Unpaid work is beneficial only in urban China, and it does not mediate the impact of physical disabilities on quality of life elsewhere. The findings indicate

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that promoting productive engagement alone, without improving basic public provisions, will have limited impact on improving quality of life of the aging population in developing countries. Keywords: China; India; productive aging; quality of life; paid work; unpaid work; physical disabilities; public provisions

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1.

Introduction Many countries are experiencing rising proportions of older population as a result of

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increasing life expectancy and declining fertility (United Nations, 2013). To keep this large population gainfully engaged, researchers and policymakers in developed countries advocate the idea of productive aging (Butler & Gleason, 1985; O'Reilly & Caro, 1995). It should be noted

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that engagement in work in old ages are not new given that the majority of human beings lived at subsistence levels and had to be productive in pre-welfare societies (Achenbaum, 2001). What is

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new is the disengagement from paid work in later life in industrialized countries, which emerged as a result of increased wealth, rising incomes, provision of private and public pension plans, technological changes and development of a retirement lifestyle (Costa, 1998). Paid work is found to be generally good for physical and mental well-being, but the size of its beneficial effect varies by the nature of work and social contexts (Waddell & Burton,

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2006). Research findings on the impact of unpaid work such as housework, caregiving, and volunteering on health, psychological well-being, and life satisfaction are mixed (Arpino &

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Bordone, 2014; Baker et al., 2005; Caputo et al., 2016; Glass & Fujimoto, 1994; Ho et al., 2009; Lawlor et al., 2002; Lum & Lightfoot, 2005; Roth et al., 2009). Most of these studies are

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conducted in the developed world, and we know little about the impact of work on older adults' quality of life in developing countries. Aging in developing countries poses several challenges in relation to work. First, they

have higher proportions of older populations in poverty as a result of low lifetime earnings as well as insufficient public welfare systems (Barrientos, 2015). Thus, individuals in developing countries tend to engage in some kind of productive activities till late in life (Benjamin et al., 2003; Davis-Friedman, 1991; Pang et al., 2004). Second, due to lifetime poverty and inadequate

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health infrastructure, aging populations in developing countries have relatively higher levels of age-related morbidity compared to those living in developed countries. This has important

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implications for the type of work they can perform. The nature of paid work available to older adults in developing countries requires more physical labor than non-physical labor; and unpaid work tends to be more physically demanding as well due to lower prevalence of basic amenities.

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Though individuals may derive utility from such work, the net effect of work on their wellbeing is indeterminate and worth studying. So, in this study, we examine the implications of productive

2.

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engagement on quality of life among the aging population in the context of developing countries.

Research Contexts

Our research focuses on China and India both of which have a sizable and rapidly growing aging populations. Both China and India emerged from colonial rule and internal

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turmoil in the middle of the last century. Since then, they have undergone different developmental trajectories in terms of social, political, and economic changes. Both achieved

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great gains in the welfare of its population, but China has substantially outperformed India. For example, while life expectancy at birth increased from 36.6 in 1950-1955 to 65.5 years in 2005-

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2010 in India, it went up from 43.4 to 74.4 years in the same period in China. Life expectancy at age 65 in 2005-2010 was also higher in China (United Nations, 2015). Much of the improvement in life expectancy in China occurred before 1980 when both countries were undergoing similar economic growth rates. Scholars like Dreze and Sen (1990) argue that the main factor that put China ahead of India is therefore not differences in economic growth rate but its significant lead in public provisions.

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One area of public provisions that has important implications for the lives of the aging population is access to health services over their lifetime. A distinguishing aspect of the Chinese

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system of public provisions is the development of a vast network of health services (Dummer & Cook, 2008; Ma & Sood, 2008; Yip & Mahal, 2008). In China, the government in urban areas and the commune in rural areas owned and financed most of the health infrastructure. This

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enabled near universal access to health care for the population. Furthermore, the Chinese primary health care system gave precedence to preventive medicine over therapeutic medicine.

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Historically, the public financing of health in India has been very low as public health expenditure is to a great extent financed through revenues from the states (Purohit, 2001). India made the first attempt to develop a primary health care system only after the first National Health Policy in 1983. However, its implementation was ineffective and India’s public health infrastructure remains underdeveloped and poorly funded. The economic reforms of the 1990s

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further eroded public provision of health care in India (Ma & Sood, 2008). The Chinese health system, though well developed compared to India, also had its

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shortcomings. The economic reforms that began in the late 1970s in China resulted in significant changes to the healthcare system that exacerbated the rural-urban divide in public provisions

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(Duckett, 2012). The role of the central government in financing healthcare shrank drastically and the main responsibility was shifted to the provincial and local governments. However, the local governments’ ability to finance healthcare varied with the unevenness of economic development across provinces (Park et al., 1996; Wong & Bird, 2008; Zheng & Hillier, 1995). Two other important areas of public provisions relevant for the aging population are health insurance and pension. India is among the very few countries in the world without an obligatory public health insurance program. There are a few schemes for the poor, but coverage

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is restricted to those younger than 65 years (Ministry of Health and Family Welfare, 2005). A 2011 survey of individuals aged 45 and above found that only 1% of respondents reported

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“insurance” as their primary payer of health care costs (Arokiasamy et al., 2012). Until the 1980s, rural Chinese residents had near universal access to primary health care through the Cooperative Medical Systems (CMS) financed mainly by the commune. With the

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collapse of CMS, most rural residents lost their only source of health insurance coverage.

Decades later in 2002, the Chinese government decided to launch a New Cooperative Medical

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Scheme (NCMS), jointly financed by the central government, local governments, and individual households. By the end of 2008, the NCMS covered 91.5% of the rural population (Xu et al., 2009). However, there is no clear evidence that the NCMS has improved health outcomes or decreased out-of-pocket health expenditure (Lei & Lin, 2009; Liang et al., 2012; Wagstaff et al., 2009).

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Publicly provided health insurance coverage is more widespread in urban China. The Government Insurance Scheme continued to provide generous health care coverage for

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employees of government agencies and related governmental bodies. In place of the old Labor Insurance Scheme that covered workers in state-owned and collective-owned enterprises, the

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Chinese government established a basic medical insurance scheme for urban employees (UEBMI) in 1998, and for non-employed urban residents (URBMI) in 2007. It was estimated that UEBMI and URBMI covered about 45% and 24% of urban residents respectively by October 2008 (Barber & Yao, 2010). Researchers found that the URBMI has improved medical care utilization, more so for the elderly (Liu & Zhao, 2012). Less than 10% of the population receive pension of any kind in India (World Bank, 2001). This is because more than 80% of the working population is in the informal sector that

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generally provides no old-age support. During the 1990s and early 2000s, the Indian government introduced several schemes to address the food, shelter, and health needs of older adults.

to lack of funding and public awareness (Agarwal et al., 2016).

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However, most of these programs were introduced at the state level and had limited impact due

In rural China, the elderly who are no longer able to support themselves and have no

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children to support them are provided for by the “five guarantees” system, which uses collective funds to provide food, clothing, housing, medical care and burial expenses. Under the civil

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service pension system, employees of government agencies and related governmental bodies are entitled to a generous government-subsidized pension on retirement that required no contribution from their end until very recently. These government jobs concentrate in urban China. The Labor Insurance Scheme had provided pension to urban retirees who were employees of state-owned or collective-owned enterprises. The 2005 One-percent Population Sample data showed that while

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pension was the primary source of support for 45% of urban elderly, only 4.6% of their rural counterparts enjoyed this luxury (Giles et al., 2010).

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According to the World Bank’s new global poverty line at 1.9 dollars a day and the 2011 Purchasing Power Parity (PPP) data, 1.85% of the Chinese population are in extreme poverty in

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2013, compared with 21.23% of the Indian population in 2011. Although poverty has declined substantially in China, the rural elderly as a group remain poorer than younger rural residents and much poorer than the urban elderly (Cai et al., 2012). Based on estimates using the 2006 China Urban and Rural Elderly Survey, as high as 29% of rural elderly and only 5.5% urban elderly have consumption levels below the One-Dollar-A-Day line (Cai et al., 2012). In India, paradoxically, the elderly households have similar poverty rates to the non-elderly households in

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most states, due to consumption-mortality differentials and a survivorship bias (Pal & Palacios, 2011).

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Presence of poverty and lack of health services, pension, and health insurance explain the continued labor force participation in old ages in India and rural China. In India, census data indicated that 42% of adults aged 60 and above and 22% of adults aged 80 and above still

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participated in the workforce in 2011 (Government of India, 2011). A survey of rural Chinese older adults in 2000 showed that about 90% of those aged between 50 and 59, 68% of those aged

2004).

3.

Research Questions

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between 60 and 69, and 19% of those aged 70 and over were still in the labor force (Pang et al.,

Since late 20th century, subjective wellbeing and quality of life have attracted increasing

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attention from both researchers and policymakers, as both try to go beyond the traditional reliance on economic indicators and the then dominant post-war societal focus on materialistic

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improvements (Bowling & Windsor, 2001). Quality of life is a multidimensional portrait of an individual’s state of health and wellbeing (Bowling, 2004; Hagerty et al., 2001). Self-assessed

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measures of quality of life have been associated with development of disease, disability and mortality, and are now considered as key parameters in the process of policy making, allocation of services and provision of care (Clifton & Gingrich, 2007; Mossey & Shapiro, 1982; Wannamethee & Shaper, 1991). However, there is a dearth of research on the implications of productive engagement on quality of life of older individuals (Taylor & Bengtson, 2001). Previous research in developed countries has consistently shown that the two most important determinants of quality of life are health and functional status (Bowling, 1995;

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Kunzmann et al., 2000; Michalos et al., 2000) and level of income (Bowling, 1995; Bowling & Windsor, 2001; Farquhar, 1995). In developing countries, the trade-off between health and

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financial needs in old ages plays a crucial role in determining participation in work. Hence, to understand the effect of productive engagement on quality of life in the developing world, it is necessary to examine how productive engagement mediates the relation between physical

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conditions and quality of life, given household economic conditions. Moreover, since quality of life can be defined as an outcome of unequally distributed living conditions and social

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environments, it is a useful tool to understand the link between welfare systems and inequality in later life (Motel-Klingebiel, 2007). Because public provisions are intertwined with variations in economic and psychosocial wellbeing, it is also important to understand how the mediating role of productive engagement varies with the level of public provisions. As highlighted earlier, there are significant differences in the level of public provisions between China and India and between

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rural and urban China. Further, health services are unevenly distributed among rural and urban India, and pension, which is mostly associated with formal employment, is also concentrated in

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urban areas. We exploit these differences in public provisions across these four societies – rural and urban China and India - to examine the effect of public provisions on the mediating role of

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productive aging on quality of life.

We start by first asking, do physical disabilities affect the propensity to work differently

across different contexts of public provisions? We hypothesize that all else being equal, individuals with higher physical disabilities will be less likely to work. However, the propensity to work will differ across rural and urban China and India, and by the nature of work. Second, do physical disabilities affect quality of life differently across contexts? We hypothesize that the gradient of physical disabilities in quality of life will be flatter in urban China than in other

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societies. Third, what role does productive engagement play in mediating the effect of physical disabilities on quality of life across contexts? We hypothesize that the mediated effect of

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physical disabilities on quality of life via paid and unpaid work is smaller in urban China than in

4.

Methods

4.1.

Data

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other societies.

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This paper uses data from the first wave of the World Health Organization (WHO) Study on Global Ageing and Adult Health (SAGE), a longitudinal and cross-national study. The target population of SAGE Wave 1 consisted of all individuals aged 50 and over, and a sample of one individual aged 18-49 years in the selected household. All participating countries implemented standardized SAGE survey instruments and a multistage cluster sampling design resulting in

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cross-country comparability and nationally representative samples. In China, the survey was carried out in 2008-2010 in Guangdong, Hubei, Jilin, Shaanxi,

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Shandong, Shanghai, Yunnan, and Zhejiang. The India sample was collected in 2007-2008 in the states of Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh, and West Bengal. We

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restricted the sample to respondents aged 50 and above and conducted all analyses separately for urban China, rural China, urban India, and rural India. This left us with an original sample of 6,567 urban Chinese, 6,800 rural Chinese, 1,861 urban Indians, and 5,289 rural Indians.

4.2.

Quality of life We measured quality of life using the WHO Quality of Life scale (Power et al., 2005)

which assesses an individual’s life quality in psychological, physical, social, and environmental

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domains. While researchers have stressed on the importance of subjective measures of quality of life (Bowling & Windsor, 2001), the WHO quality of life scale was developed specifically to

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create a measure that is valid across cultures (Skevington, 2002). The questionnaire has been modified for use in older subpopulations and successfully applied to aging research (Schmidt et al., 2006). We recoded all eight items so that higher values indicate better quality of life, then

Productive engagement

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4.3.

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added up the values over the items and rescaled the total scores from 0 to 100.

Sherraden et al. (2001) suggest four categories of productive engagement that are relevant in the context of developing countries. These four categories are market activities (such as employment), non-market activities with economic value (such as taking care of older family members or young children), formal social and civic activities (such as volunteering in schools

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and social services agencies), and informal social assistance (such as helping neighbors). They go beyond the usual focus in developed countries on volunteerism and allow researchers to

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differentiate the concept of productive aging from other related terms such as successful aging (Lum, 2013). Following these guidelines, we focus on paid and unpaid work. We identify paid

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work as participation in any activities for which the respondent is paid in cash or in kind, or work on the family farm or business for at least two days in the last seven days from the date of survey. We define unpaid work as involvement in household chores, watching children, and providing care to someone (including those outside home) on an average day. The information on unpaid work is based on the Day Reconstruction Module, a method incorporating features of time-budget measurement and experience sampling that allows participants to reconstruct their activities with reduced errors and biases of recall (Kahneman et al., 2004). The survey asked a

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random quarter of the total sample about their activities during a specific time of the day

4.4.

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(morning, afternoon, evening, or whole day).

Physical disabilities

We derived the measure of physical disabilities from the 12-item version of WHO

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Disability Assessment Scheme (Üstün et al., 2010). It includes questions on activities of daily living and instrumental activities of daily living and produces an overall score that can be used to

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identify health needs and changes in physical function over time. The items use a 1-5 Likert Scale to measure how much difficulty the respondents had doing the activities. We took the sum over the 12 items and obtained an index ranging from 12 to 60. For our regression analyses, we recoded the physical disabilities index into three country specific equally distributed categories:

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low, middle, and high.

Final analytical sample

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We dropped cases with missing values in quality of life, physical disabilities, or engagement in paid and unpaid work, resulting in a sample of 5,659 urban Chinese, 6,271 rural

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Chinese, 1,568 urban Indians, and 4,498 rural Indians. We further excluded the cases with missing values in any of the control variables (introduced below). The percent of missing values in control variables ranges between 0 and 6.7% across the four subsamples. Our final analytical sample consists of 5,016 urban Chinese, 5,746 rural Chinese, 1,362 urban Indians, and 4,097 rural Indians. As a sensitivity test, we conducted multiple imputation by chained equations to impute missing values in the control variables separately for each subsample. The results are consistent with what we present here in the paper.

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4.6.

Analytical strategy

use the following reduced form specification: =

+

refers to paid and unpaid work.

+



(1)

includes several control variables.

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where

+

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We begin our analysis by first studying how disability affects probability of work. We

Studies have shown that participation in paid or unpaid work is highly gendered, with men

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associated with the former and women the latter (Chang et al., 2011). Therefore, we control for gender with a dummy variable of “1” indicating female and “0” male. Age is another important factor shaping health and participation in productive engagement (Benjamin et al., 2003). We measure age in three categories: 50–59, 60–69, and 70 and above. Respondents’ own education

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is measured in terms of years of schooling. As noted earlier, financial need is a push factor for productive engagement among older adults in developing countries. To control for the household’s financial status, we use a measure derived from the household’s ownership of

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durable goods, characteristics of the dwelling, and access to services such as water, sanitation and cooking fuel. To control for respondents’ family background, we include father’s education

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measured in three categories: no formal education, less than primary education, and primary education and above. The next set of control variables captures both the need for paid work and unpaid work and access to potential social support. For example, while living with a grandchild may increase the burden of caregiving on older adults, it may also enhance their subjective wellbeing. We define living arrangements based on whether the person coresides with spouse, children, and/or grandchildren. Social involvement index captures respondents’ involvement in the community, such as attending public meetings, socializing with coworkers outside of work,

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and attending religious services, among others. In addition, we control for the assignment of reference time with regard to the Day Reconstruction Module, and province fixed effects for also includes unpaid work when the dependent

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China and state fixed effects for India.

variable is paid work and vice versa. Regressions are run separately for rural and urban China and India.

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Subsequently, to understand the mediating effect of work on quality of life, we first

examined how quality of life is affected by physical disabilities using the following specification

Where

+

+

is quality of life and

+

(2)

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=

is similar to

above, except that it excludes any

control for paid or unpaid work. Following this, we introduce work in the regression =

+

+ Σ !

+

+

(3)

Kenny (1986).

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The above relations can be visualized as a mediation model proposed by Baron and

FIG. 1. ABOUT HERE

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For mediation to be present, Baron and Kenny (1986) argued that

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in (3) are significantly different from 0, and In linear models, mediation is defined as

in (2),

in (3) is either 0 or less than

×! =

in (1) and !

in absolute value.

− ′ (Judd & Kenny, 2010; MacKinnon et

al., 2004). However, it is not possible to make similar conclusions for non-linear mediators. Further, it is difficult to make a causal interpretation of this relationship as contemporaneous data can be subject to unobserved confounding. To address these issues, in this study we pursue the potential outcomes framework introduced by Imai and others (Imai et al., 2010a; Imai et al., 2010b). The strength of the

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potential outcomes framework is that it uses counterfactuals to identify causal effects. The average causal mediation effects (ACMEs) are defined as the mean difference in effect between

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two counterfactual states of a mediator, assuming no change in the initial condition. Similarly, the average direct effect (ADE) is the mean difference between two counterfactual states of initial conditions, assuming no change in the mediator.

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We perform the mediation analysis using the user written code -medeff- in STATA 14 (Hicks & Tingley, 2011). The codes make necessary adjustments for nonlinear models and report

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total, direct, indirect and ACMEs and their standard errors using a bootstrapping approach (MacKinnon et al., 2007; MacKinnon et al., 2004). Imai et al. (2011)’s framework requires that the initial conditions are assumed to be unrelated to unobserved confounders and the observed mediator is assumed to be unrelated to potential confounders once the initial conditions and unobserved confounders are taken into account. We conduct a sensitivity test to check the

Results

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5.

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validity of these assumptions.

Table 1 shows the summary statistics of our study sample. On average, urban Chinese

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older adults report the highest quality of life (66.42 out of 100), followed by urban Indian older adults (64.43), rural Chinese older adults (63.91), and rural Indian older adults (60.7). Consistent with the better health profile and health care provision in China than in India as discussed earlier, urban Chinese older adults (14.9 out of 60) and rural Chinese older adults (16.04) score much lower than their urban Indian (21.88) and rural Indian counterparts (23.86) on the WHO disability assessment schedule.

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Consequences of the differences in public provisions can be seen in respondents’ age distributions that reflect greater longevity among the Chinese population compared to the Indian

educational gain in China compared to India.

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population, and in urban areas compared to rural areas. Also notable is the intergenerational

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TABLE 1 ABOUT HERE

While less than one fifth (17%) of urban Chinese older adults engage in paid work, more

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than 60% of rural Chinese older adults, 35% of urban Indian older adults, and 46% of rural Indian older adults do so. The differences in the prevalence of paid work among these older adults likely reflect the greater access to pension, and hence, lesser need to continue earning among urban Chinese relative to their counterparts in rural China and in India. Another reason

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for the low level of engagement in paid work among urban Chinese may be the national mandatory retirement ages (60 for men and 55 for women with some exceptions), which apply to relatively more urban Chinese than rural Chinese. Across these four societies, however, the

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levels of engagement in unpaid work are quite similar, ranging from 55% to 63%. Another important contextual difference lies in the nature of coresidence. About three-

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fourths of older adults live together with their spouse, but the prevalence of coresidence with a child or a grandchild differs greatly across societies. In urban China, 45.3% of older adults live with a child. In contrast, as many as about 88% of Indian older adults and only about 36% of rural Chinese older adults coreside with a child. While 59% of rural Indian and 51% of urban Indian older adults coreside with a grandchild, only 17% of urban Chinese and 23% of rural Chinese older adults do so. These differences are to be understood within the social and demographic contexts of China and India. Total fertility rates in China started declining rapidly

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even before the implementation of One Child policy and has remained lower than that in India since 1971. As a result, our Chinese respondents on average have fewer children and

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grandchildren than Indian respondents. Moreover, there has been large-scale labor outmigration of adult children from rural China over the past few decades. Many older rural Chinese are left to care for grandchildren and farmland while their adult children work in the urban areas seeking a

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better life (All China Women's Federation, 2013). On the other hand, despite its economic

development, India remains a largely rural society and three-generation households are common.

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This is partly due to the sociocultural factors such as sub-caste networks that restrict mobility (Munshi & Rosenzweig, 2009). Unsurprisingly, we find the highest level of engagement in both paid and unpaid work among older rural Chinese.

5.1.

The effects of physical disabilities on productive engagement

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To address the first research question on how physical disabilities affect participation in paid and unpaid work, we present the results of logistic regression models on paid work in the

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first panel, and those on unpaid work in the second panel in Table 2. TABLE 2 ABOUT HERE

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Understandably, physical disabilities have a strong and negative impact on the likelihood

of participation in paid work or unpaid work. Relative to their counterparts with low-level physical disabilities, urban Chinese, rural Chinese, and Indians with middle-level physical disabilities are 35%, 53%, and 28%, respectively, less likely to engage in paid work. Moreover, urban Chinese, rural Chinese, and Indians with high-level physical disabilities are 43%, 70%, and 58%, respectively, less likely to participate in paid work than their counterparts with low-level

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physical disabilities. These patterns support our hypothesis about the gradient of physical disabilities in productive engagement.

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In urban China and rural India, those with high-level physical disabilities are 27% to 29% less likely to engage in unpaid work than their counterparts with low-level physical disabilities. However, contrary to our hypothesis, physical disabilities do not seem to affect the likelihood of

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participation in unpaid work in rural China. This may be explained by the massive labor outmigration of adult children. Due to the unavailability of other helping hands in the household,

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older rural Chinese with high-level physical disabilities simply do not have the option of reducing their involvement in household chores and care work.

5.2.

Does productive engagement promote quality of life for everyone? To investigate the mediating role of work in linking physical disabilities with quality of

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life, we first present the results of OLS regression models in Tables 3a and 3b. Model 1 shows the total effect of physical disabilities on quality of life. Overall, higher physical disabilities are

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associated with lower quality of life in all four societies. However, the variation in quality of life by the level of physical disabilities is greatest in rural India and smallest in urban China. The

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findings show that health status does not generate as much variation in quality of life in urban China as it does in rural China and India, consistent with our hypothesis about the relationship between physical disabilities and quality of life. TABLES 3a and 3b ABOUT HERE

In Model 2, we add both paid work and unpaid work. Participation in paid work promotes quality of life in all four contexts. Performing unpaid work is positively associated with quality of life in urban China but not in other societies. These patterns partly confirm our hypothesis

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about the higher returns on work in terms of quality of life in urban China, compared with the other three contexts. We also observe that the magnitude of the effects of physical disabilities on

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quality of life declines by a small amount after paid work and unpaid work are added to the model. This indicates that the role of paid work and unpaid work in alleviating the negative effects of physical disabilities on quality of life might be quite limited.

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In Models 3 and 4, we test whether the effect of paid work and unpaid work on quality of life differ by the level of physical disabilities. The results show that older urban Chinese,

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regardless of their level of physical disabilities, benefit from paid work. Yet, in rural China, the effect of paid work on quality of life is negative among those with low-level physical disabilities and positive among those with high-level physical disabilities. In India, only older adults with high-level physical disabilities benefit from paid work. These findings suggest that in rural China, urban India, and rural India where public support for older adults with high-level physical

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disabilities are most needed yet highly restricted, paid work will make a greater difference in improving their quality of life.

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Similarly, we observe a statistically significant positive effect of unpaid work on quality of life among older adults with high-level physical disabilities across contexts. Given the

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restrictions imposed by high-level physical disabilities on their capability to do paid work, doing unpaid work may be the only option for these older adults in order to obtain support from their family members or other people. Among older adults with low-level physical disabilities in rural China and urban India, unpaid work may simply mean more chores since they do not have to solely rely on unpaid work in exchange for support. To identify how paid and unpaid work mediate the effects of physical disabilities on quality of life, we present the results from causal mediation analyses in Table 4.

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TABLE 4 ABOUT HERE The results confirm a negative and statistically significant effect of physical disabilities

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on quality of life. Moreover, consistent with the results of OLS regression models in Tables 3a and 3b, the average direct effects of physical disabilities on quality of life are smallest in urban China and largest in rural India.

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As the first panel in Table 4 shows, in all four contexts, the average causal mediation effect of low-level physical disabilities is positive and that of high-level physical disabilities is

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negative, and both are statistically significant at the 95% level. The results imply that low-level physical disabilities improve quality of life on average by increasing participation in paid work, while high-level physical disabilities reduce quality of life on average by decreasing participation in paid work. However, the estimated average direct effects are much larger than the estimated

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average mediation effects. As a result, the estimated proportions mediated are below 3% across contexts. It is worth noting that the difference in quality of life due to the change in participation in paid work induced by physical disabilities is smaller in urban China than in rural China, urban

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India, or rural India. The findings therefore support our argument that physical disabilities generate lesser variation in terms of quality of life in urban China than in the other three contexts.

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The second panel of Table 4 presents the results for unpaid work as the mediator. Unlike

what was observed for paid work as the mediator, the estimated average mediation effects are small and statistically non-significant. The only exception is that high-level physical disabilities reduce quality of life on average by reducing participation in unpaid work among urban older Chinese, which accounts for less than 1% of the average total effect. The insignificant role of unpaid work in transmitting the impact of physical disabilities on quality of life implies that

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trying to improve the quality of life of older adults in developing countries by promoting their participation in unpaid work is likely a misplaced hope.

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Figures S1-S4 in the Supplementary Materials illustrate the results from the sensitivity analysis. Overall, the patterns suggest that relatively small departures of the sensitivity parameter from 0 result in statistically significant effects in either a positive or negative direction for both

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the paid work pathway and the unpaid work pathway for all four contexts. We caution against

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making strong conclusions about the mediated effects of paid or unpaid work.

Discussion

Since the 1980s, gerontologists have argued that if people remain productive in later life, it will result in a sense of purpose and higher sense of wellbeing. However, socioeconomic status can potentially have an important impact on one’s ability to experience productive aging (Estes

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& Mahakian, 2001). There is a need for a political economy perspective to understand the socially and structurally produced nature of aging. Lum (2013), reviewing research on

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productive aging observes that “productive aging should be seen as a choice, not an obligation for older people; otherwise, the productive aging agenda will be seen as exploiting older people.”

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This issue becomes a moot point in the developing world where work is often by necessity and there is higher prevalence of age related morbidity. This study examines the effect of work on quality of life in China and India where the

elderly often continue to work due to economic needs. Since quality of life is primarily determined by health and material resources, and the propensity to work in old ages is a tradeoff between these two factors, we investigate the mediating effect of work on quality of life among individuals with different levels of physical disabilities controlling for material resources.

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Though older urban Chinese, on average, enjoy better health and health care, less than one fifth of them are engaged in paid work. In rural India and particularly rural China, our evidence

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confirms the pattern of “ceaseless toil” or “working until dropping” observed in earlier studies (Benjamin et al., 2003; Davis-Friedman, 1991; Pang et al., 2004). Moreover, our study reveals a double burden of both paid and unpaid work on older adults in rural China and to a lesser extent

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in rural India. Yet, in terms of the implications on quality of life, paid work is more beneficial in urban China than in other societies, and unpaid work is only beneficial in urban China.

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We draw two main conclusions from our causal mediation analyses. First, paid work only explains a minor share (less than 3%) of the total effect of physical disabilities on quality of life in all four societies, and unpaid work does not mediate the impact of physical disabilities on quality of life in rural China and both rural and urban India. Although physical disabilities are powerful determinants of engagement in paid work, the benefits in terms of quality of life

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generated by doing paid work are quite modest among older adults in China and India. Unpaid work is even less rewarding than paid work, and perhaps unsurprisingly, participation in unpaid

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work is affected to a lesser extent by the level of physical disabilities. Second, since we have separated paid and unpaid work and also controlled for co-

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residence, we consider public provision to be the most plausible explanatory factor for the variation in the mediation effects that we observe in these four contexts. Due to overall better health services, insurance, and old-age pension benefits, older adults in urban China enjoy several advantages relative to their counterparts in rural China and both urban and rural India: physical disabilities exert a smaller negative direct effect on quality of life, physical disabilities matter less in determining participation in paid or unpaid work, and performing paid or unpaid work generates greater benefits in terms of quality of life. These observations are very pertinent

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for the developing country context. Turning this point around, we can reasonably speculate that for productive aging to be effective it is necessary that the government step up public provisions.

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To further examine our findings, we have explored other different specifications. By pooling the Chinese and Indian samples together, we show that the interaction terms between societal dummies and physical disabilities are statistically significant and negative (Table A3),

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which supports our finding that physical disabilities generate lesser variation in quality of life in urban China than in the other three societies. However, our observation that paid work is more

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beneficial in urban China than in the other three contexts is not supported by the pooled sample analysis (Table A3). Since both paid work and unpaid work are highly gendered in China and India, we also did separate gender subsamples. The patterns are largely consistent with our finding that work in general is more beneficial in urban China than in other three contexts (Table B3). We dealt with the gendered nature of work and its implications on older adults’ lives in

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greater details in another paper (Hu & Das, 2018). Furthermore, we differentiated formal and informal paid work, and the results show that

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the informal labor sector is indeed very large in urban India and particularly rural India and rural China (Table D1). Within paid work, formal paid work seems to be more beneficial than

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informal paid work in urban China, urban India, and rural India, while only formal paid work but not informal paid work has a significant positive effect on quality of life in rural China (Table D3). Additional analyses of subsamples by household financial status show that the modest positive mediated effects via doing paid work are stronger among those with low financial limitations than among those with high financial limitations, and that the negative mediated effects via disengagement in paid work are stronger among those with high financial limitations than among those with low financial limitations (Table E1); this is particularly true in both urban

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and rural India and rural China. These patterns suggest that productive engagement tends to exaggerate the gaps in quality of life due to physical disabilities because it benefits those already

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advantaged (with low financial limitations) more than it benefits their counterparts. While this study applies causal mediation analysis to overcome the limitations of the classical mediation analysis, we caution against over-interpretation. First, we acknowledge that

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there can be reverse causality between quality of life and physical health which may influence the mediating role of work. This is an important limitation and can only be addressed in a

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longitudinal framework when the next wave of data is available. Second, our measures of engagement in paid and unpaid work are not perfect due to the survey design, which collects information on paid work over the past seven days and unpaid work during the past day. This design, however, may reduce recollection bias that aging surveys commonly face. Third, the results from our formal sensitivity analysis suggest that omitted factors may bias the reported

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magnitude of the mediating effect of the paid work pathway. Even with these limitations, our study contributes to the current literature in several ways.

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First, to our knowledge, this is the first study to investigate the mediating role of productive engagement between physical disabilities and quality of life among older adults. The findings

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suggest that productive engagement is somewhat beneficial, but its impact on quality of life should not be exaggerated or assumed. It is also important to distinguish paid work from unpaid work, for both are affected differently by physical disabilities and both affect quality of life differently.

Second, it highlights the importance of public provisions by showing that productive aging can be potentially beneficial when the government meets the basic needs of older adults. The Social Protection Index, constructed by the Asian Development Bank using 2009 data on

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government social insurance, social assistance, and other social protection programs, shows that China and India spend relatively low on social protection—only 2.7% and 1.3% of GDP

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respectively. Not surprisingly, both China and India rank low on both the index and its individual components, compared with other Asian countries in the same income group. For both rural China and India as a whole, a productive aging agenda can only be successful when the

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populations receive better public provisions of health services and old-age security. The results from this study provide a valuable reference for other developing countries facing rising aging

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populations.

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Fig. 1. The Mediation Model

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Table 1 Descriptive statistics of the analytical sample

Female (%) Age group (%) 50-59 60-69 70+ Years of schooling (mean)

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Father's education (%) No formal education Less than primary Primary and above Index of household assets (mean)

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Observations (N) Standard deviations in parentheses

Urban India 64.43 (13.24) 21.88 (7.83) 35.24 55.14 75.33 88.55 50.66 -0.01 (0.90) 50.37

Rural India 60.7 (14.87) 23.86 (8.70) 45.74 59.78 76.42 87.67 59.38 0.06 (0.89) 46.03

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Paid work (%) Unpaid work (%) Spouse in the household (%) Child in the household (%) Grandchild in the household (%) Index of social involvement (mean)

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WHO disability assessment schedule (mean)

Rural China 63.91 (14.81) 16.04 (5.49) 60.22 63.33 77.1 35.92 22.66 0.1 (0.81) 49.60

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Quality of life (mean)

Urban China 66.42 (13.45) 14.9 (4.73) 17.26 63.24 77.47 45.3 16.81 -0.08 (0.79) 54.98 40.85 28.71 30.44 7.75 (4.55)

47.55 30.94 21.51 3.56 (3.41)

45.96 34.73 19.31 6.18 (5.52)

45.5 34.73 19.77 2.93 (4.24)

53.71 28.27 18.02 0.14 (0.44) 5016

81.33 16.01 2.66 -0.14 (0.43) 5746

44.79 32.75 22.47 1.01 (0.41) 1362

73.05 20.87 6.08 0.65 (0.48) 4097

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Table 2 Odds ratios of logistic regression models predicting paid work and unpaid work

High Number of observations

Urban China Physical disabilities Middle

0.96 (0.08) 0.71*** (0.07) 5,016

High Number of observations

Rural India

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0.65*** (0.07) 0.57*** (0.08) 5,016

0.47*** 0.71** (0.05) (0.12) 0.30*** 0.41*** (0.03) (0.08) 5,746 1,362 Unpaid work Rural China Urban India 1.07 (0.09) 0.97 (0.09) 5,746

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Physical disabilities Middle

Paid work Rural China Urban India

1.21 (0.19) 0.76 (0.13) 1,362

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0.70*** (0.06) 0.41*** (0.04) 4,097

Rural India 0.96 (0.09) 0.73*** (0.07) 4,097

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Note: All models control for gender, years of schooling, age group, presence of spouse in the household, presence of child in the household, presence of grandchild in the household, social involvement index, father's education, the index of household assets, province or state dummies, and assignment of set A, B, C, or D of the Day Reconstruction Module. In addition, the models on paid work control for unpaid work, and vice versa. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Table 3a Results of OLS regression models predicting quality of life (urban China and rural China) Urban China Rural China Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 Physical disabilities (PD) Middle -4.57*** -4.42*** -4.39*** -4.74*** -4.83*** -4.73*** -5.94*** -5.41*** (0.39) (0.39) (0.43) (0.63) (0.41) (0.42) (0.82) (0.66) High -11.41*** -11.20*** -10.95*** -13.35*** -14.05*** -13.87*** -16.74*** -16.61*** (0.54) (0.54) (0.56) (0.81) (0.48) (0.48) (0.84) (0.76) 2.57*** 2.92*** 2.41*** 1.06** -1.36* 0.91** Paid work (0.48) (0.60) (0.48) (0.44) (0.82) (0.44) Unpaid work 1.08*** 1.11*** -0.05 -0.01 -0.14 -2.00*** (0.39) (0.39) (0.55) (0.41) (0.41) (0.67) 0.04 1.43 PD (Middle)*Paid work (0.94) (0.94) PD (High)*Paid work -2.19 4.66*** (1.45) (0.99) PD (Middle)*Unpaid work 0.45 1.24 (0.75) (0.82) PD (High)*Unpaid work 3.48*** 4.30*** (0.93) (0.91) Constant 69.44*** 67.67*** 67.52*** 68.45*** 71.28*** 70.30*** 72.21*** 71.44*** (1.00) (1.05) (1.05) (1.08) (0.96) (1.07) (1.20) (1.10) R-squared 0.258 0.263 0.264 0.266 0.301 0.302 0.306 0.306 Observations 5,016 5,016 5,016 5,016 5,746 5,746 5,746 5,746 Note: All models control for gender, years of schooling, age group, presence of spouse in the household, presence of child in the household, presence of grandchild in the household, social involvement index, father's education, the index of household assets, province or state dummies, and assignment of set A, B, C, or D of the Day Reconstruction Module. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Table 3b Results of OLS regression models predicting quality of life (urban India and rural India) Urban India Rural India Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 Physical disabilities (PD) Middle -6.30*** -6.19*** -6.40*** -7.78*** -7.51*** -7.39*** -7.84*** -7.97*** (0.63) (0.63) (0.80) (0.97) (0.43) (0.43) (0.64) (0.66) High -13.77*** -13.56*** -14.79*** -15.60*** -15.78*** -15.48*** -16.60*** -16.66*** (0.84) (0.84) (0.97) (1.23) (0.48) (0.48) (0.63) (0.74) 1.67** 0.62 1.64** 1.56*** 0.51 1.50*** Paid work (0.69) (0.88) (0.69) (0.42) (0.64) (0.42) Unpaid work -0.09 -0.18 -1.86** 0.54 0.48 -0.39 (0.67) (0.67) (0.90) (0.42) (0.41) (0.60) 0.28 0.67 PD (Middle)*Paid work (1.27) (0.84) PD (High)*Paid work 5.02*** 2.75*** (1.75) (0.95) PD (Middle)*Unpaid work 2.82** 0.97 (1.27) (0.85) PD (High)*Unpaid work 3.74** 1.95** (1.54) (0.91) Constant 70.99*** 69.94*** 70.64*** 70.93*** 72.56*** 70.93*** 71.68*** 71.54*** (1.83) (1.93) (1.95) (1.95) (1.09) (1.19) (1.23) (1.21) R-squared 0.407 0.410 0.414 0.413 0.421 0.424 0.425 0.424 Observations 1,362 1,362 1,362 1,362 4,097 4,097 4,097 4,097 Note: All models control for gender, years of schooling, age group, presence of spouse in the household, presence of child in the household, presence of grandchild in the household, social involvement index, father's education, the index of household assets, province or state dummies, and assignment of set A, B, C, or D of the Day Reconstruction Module. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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% of total effect mediated

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Table 4 Mediated effects of Physical disabilities (PD) on quality of life via paid work and unpaid work Average causal mediation effect Average direct effect Total effect PD (low versus middle & high) via paid work Urban China 0.14 (0.07,0.22) 6.37 (5.59,7.14) 6.51 (5.71,7.28) Rural China 0.22 (0.11,0.34) 7.98 (7.12,8.81) 8.19 (7.34,9.02) Urban India 0.19 (0.05,0.38) 9.14 (7.91,10.36) 9.33 (8.11,10.56) Rural India 0.22 (0.12,0.34) 11.02 (10.21,11.82) 11.24 (10.43,12.04) PD (high versus middle & low) via paid work Urban China -0.08 (-0.17,-0.01) -8.33 (-9.19,-7.5) -8.41 (-9.28,-7.58) Rural China -0.16 (-0.26,-0.06) -10.59 (-11.36,-9.85) -10.75 (-11.51,-10.01) Urban India -0.23 (-0.45,-0.06) -10.55 (-11.96,-9.18) -10.78 (-12.18,-9.4) Rural India -0.28 (-0.41,-0.15) -11.53 (-12.36,-10.72) -11.81 (-12.63,-10.99) PD (low versus middle & high) via unpaid work Urban China 0.03 (-0.01,0.08) 6.37 (5.59,7.14) 6.4 (5.61,7.17) Rural China 0 (-0.02,0.01) 7.98 (7.12,8.81) 7.97 (7.12,8.8) Urban India -0.01 (-0.09,0.04) 9.14 (7.91,10.36) 9.13 (7.89,10.35) Rural India 0.01 (-0.02,0.05) 11.02 (10.21,11.82) 11.04 (10.22,11.84) PD (high versus middle & low) via unpaid work Urban China -0.07 (-0.15,-0.02) -8.33 (-9.19,-7.5) -8.4 (-9.26,-7.57) Rural China 0 (-0.02,0.02) -10.59 (-11.36,-9.85) -10.59 (-11.36,-9.85) Urban India 0.03 (-0.11,0.17) -10.55 (-11.96,-9.18) -10.52 (-11.93,-9.15) Rural India -0.04 (-0.11,0.02) -11.53 (-12.36,-10.72) -11.57 (-12.41,-10.76) 95% Confidence Interval in parentheses

2.09 (1.87,2.39) 2.63 (2.39,2.94) 2.02 (1.78,2.32) 1.92 (1.8,2.07) 0.99 (0.89,1.1) 1.46 (1.36,1.56) 2.16 (1.91,2.47) 2.33 (2.18,2.5)

0.45 (0.41,0.52) -0.03 (-0.04,-0.03) -0.12 (-0.14,-0.11) 0.1 (0.09,0.1) 0.87 (0.79,0.96) -0.01 (-0.01,-0.01) -0.24 (-0.28,-0.22) 0.37 (0.34,0.39)

ACCEPTED MANUSCRIPT Acknowledgements

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The authors would like to acknowledge Pei-Chun Ko and Ariane Juliana Utomo for their comments on an earlier draft of the paper and Saharah Bte Abubukar for her editorial assistance.

ACCEPTED MANUSCRIPT Research highlights

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In developing countries, both paid and unpaid work is common out of necessity. Older adults bear a double burden of paid and unpaid work in rural China and India. Productive aging has limited impact on quality of life in developing countries. Impact of productive aging on quality of life varies with public provisions. Physical disabilities associated with aging is less consequential in urban China.

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