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Asia Research Institute Working Paper Series No. 267

Work in Old Age and Quality of Life: Gender Divide in China and India

Shu Hu Asia Research Institute & Centre for Family and Population Research

National University of Singapore [email protected]

Dhiman Das [email protected]

AUGUST 2018

mailto:[email protected]


ARI Working Paper No. 267 Asia Research Institute ● Singapore

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Work in Old Age and Quality of Life: Gender Divide in China and India

Shu Hu

Asia Research Institute & Center for Family and Population Research, National University of Singapore

[email protected]

Dhiman Das

[email protected]

ABSTRACT In this study, we examine the implications of gendered pattern of work on quality of life among older adults aged 50 and above in China and India. We use a causal mediation analysis approach to data from the WHO Study on Global Ageing and Adult Health (2007/10). Our results show that both paid and unpaid work are least gendered in urban China and most in rural India with an exceptionally heavy double burden on rural Chinese women. Compared to men, women have lower quality of life due to less involvement in paid work, more so in India than in urban China. In rural China, greater involvement in unpaid work reduces women’s quality of life. The results highlight the importance of better public provision and greater gender equity in improving quality of life among women in later life.




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INTRODUCTION In this study, we focus on gendered pattern of work in later life and its effect on quality of life. We examine this issue in the context of developing countries where low lifetime earnings and savings and inadequate public welfare system often push people to be engaged in some type of work till later in life. Further, the gendered nature of work participation with male engagement in paid work and female engagement in unpaid work usually results in conditions unfavorable to women. This is particularly relevant as aging population is disproportionately female. An additional factor related to work participation is the gendered nature of morbidity in later life (John & Beth, 2003). This has important consequences on individuals’ ability to work and the effect of work on their quality of life. Our comparative study of China and India is motivated by the similarities and differences between the two countries. China and India are the two most populous developing countries with sizable and rapidly growing aging population. The gendered division of work is likely to have different implications in China compared with India, due to their differences in female labor force participation, women’s right to inheritance, levels of urbanization and internal labor migration, and provision of public welfare including basic education, health care, and pension. In particular, urban China provides not only better work opportunities and pension, but also better nutrition and health care for its residents. The different developmental trajectories and institutional contexts of urban China, rural China, and India present a valuable opportunity to examine the role of public provision in shaping the gendered implications of work on older adults’ wellbeing. Our main research questions are how does gender affect participation in paid and unpaid work? How does work mediate the gender differences in quality of life in urban China, rural China, and India? And what is the role of institutional contexts including provision of welfare, health, and legal rights in explaining the dynamics of gender, work, and quality of life across these societies? To address the research questions, we use a causal mediation analysis approach to examine the effect of work on quality of life between women and men. The findings of this research will shed light on not only the mediating role of work between gender and quality of life, but also how important public provision and gender equity are to improve the quality of life of both women and men in later life. RESEARCH CONTEXTS AND HYPOTHESES Both China and India have a long and strong tradition of patriarchal and patrilineal systems and practices, which strongly discriminate against women. However, the male-female gaps in health, longevity, education, and employment have reduced at a faster rate in China than in India (Dreze & Sen, 1990; Gupta et al., 1997; Gupta et al., 2004). In particular, the Chinese government promoted female labor force participation in both rural and urban areas during the Maoist era (Cook & Dong, 2011). As a result, China achieved one of the highest female labor force participation rates in the world (Kidd & Meng, 2001). And labor force participation rate has been much higher for Chinese women than for Indian women ever since the early 1950s. After the economic reform, however, the labor force participation rate of urban Chinese women has declined over time at a faster rate than that of their male counterparts. Rural Chinese women’s labor force participation rate remained at a high level despite a recent downward trend. On the other hand, female labor force participation rates have remained low in India over the past few decades. More recently, India even experienced a slight decline in female labor force participation rates (Jayachandran, 2015).


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China’s earlier efforts in encouraging women’s participation in paid work are not accompanied by initiatives addressing the gendered division of unpaid labor including household chores and care work. However, the legacy of high female labor force participation rates coupled with better education and more equitable gender ideology in urban China may lead to a relatively more balanced division of unpaid work between men and women. Some of the most important achievements in reducing gender inequity in China did not necessarily come from a direct program but by reducing the power of family and lineage through collectivization of means of production where communes substituted for the traditional role of the family. The Communist Party also attempted to give equal rights for women in family law. The most radical step was taken in the Marriage Law of 1950, which sought to eliminate arranged marriages, bride price, and child marriage. It also upheld women’s right to enter and exit marriage and inherit property and control of their children. However, it met with strong opposition and was limited in its implementation. The 1980 New Marriage Law, again tried to uphold women’s equal rights to productive resources and inheritances and so on. However, the communist system did not touch some aspects of the family particularly those related to the care of the elderly and disabled. Child care and elderly care have remained women's responsibility (Cook & Dong, 2011). The Chinese state also left intact the system of exogamy and patrilocal residence, which are usually associated with diminished position of women within the family and society (Gupta et al., 1997; Gupta & Li, 1997). Post-reform period saw some decline in economic engagement of women. Women's work once again became invisible, eroding their intrahousehold bargaining power. Women are once again encouraged to be good daughters-in-law (Honig & Hershatter, 1988). In the face of a rapidly aging population, the Chinese state encourages stable familial support for the aged and is unlikely to alter course on this issue. In Rural China, however, the gender and generation patterns of massive labor migration to urban areas have generated feminization of agricultural labor (Chang, MacPhail, & Dong, 2011) and a growing population of left-behind children cared for by grandparents (All China Women's Federation, 2013). These changes are likely to increase the double burden on rural Chinese women in later life. In India, the pattern of men dominating work outside home and women being primarily responsible for household labor remains strong. H1: Based on these patterns, we expect that the gender difference in participation in paid work

is smallest in rural China and largest in India; the gender difference in participation in unpaid work is smallest in urban China and largest in India.

Both countries tried to address the problem of insufficient social security by shifting the responsibility to the children. Chinese citizens are obliged by law to care for their elderly. It was stated in Article 49 of China’s 1982 Constitution: “children who have come of age have the duty to support and assist their parents” (Palmer, 1995). China further introduced the Law on the Protection of the Rights and Interests of the Elderly in 1997, which stipulate that “the elderly shall be provided for mainly by their families, and their family members shall respect, care for, and look after them.” Similarly, India implemented the Maintenance and Welfare of Parents and Senior Citizens Act in 2007, obligating children and heirs to provide maintenance to parents who are unable to maintain themselves. The 2011 National Policy on Senior Citizens again emphasizes the role of the family in caring for and supporting the elderly. On the other hand, elderly parents may feel obligated to help with household labor and care for grandchildren and expect old age support from adult children in return.


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While most Indians work in the informal economy, urban Chinese have access to more regular and better-paid jobs due to a larger public sector and service sector, even though China’s reforms of state-owned enterprises in the 1990s have resulted in expansion of informal labor market. Similar to the Indians, rural Chinese in old age are likely to be engaged in agriculture and/or self-employment. Among the three societies, the elderly in urban China have better access to pension and other public welfare programs than their counterparts in rural China or India. For example, there are a pension scheme covering civil servants, a pension scheme for retirees of public service organizations, a basic endowment insurance for the urban working group, and a social endowment insurance for non-working urban residents. In contrast, the majority of Indians and rural Chinese rely on their families and adult children in particular for old age support. H2: We expect paid work to have a positive effect on quality of life, especially in the context of

urban China. However, participation in unpaid work may have mixed effects on quality of life. On one hand, due to the weak enforcement of their equal legal rights to inheritance, women are more likely to be engaged in reciprocal exchanges where they co-reside with families and support themselves through unpaid work. We might expect that performing unpaid work improve quality of life. On the other, in all three societies, as elsewhere, unpaid work is undervalued than paid work. Women’s greater involvement in unpaid work may not necessarily give them an advantage in quality of life, compared to men. Given the relatively better provision of basic education, health care, and pension and higher level of gender equity in urban China, compared to rural China and India, we expect the gender differences in quality of life generated by work among the most disadvantaged social groups are smaller in urban China than in rural China and India. H3a: Among those with low financial status, the gender gaps in quality of life due to women’s less

participation in paid work are smaller in urban China than in rural China and India. H3b: Among those with great physical constraints, the gender gaps in quality of life due to

women’s less participation in paid work are smaller in urban China than in rural China and India.

H3c: Among those with low financial status, the gender gaps in quality of life due to women’s

more participation in unpaid work are smaller in urban China than in rural China and India. H3d: Among those with great physical constraints, the gender gaps in quality of life due to

women’s more participation in unpaid work are smaller in urban China than in rural China and India.

DATA AND VARIABLES We use data from the Study on Global Ageing and Adult Health Wave 1, conducted in 2007/10 in China and in 2007 in India. The Indian survey was a follow-up of the World Health Survey (WHS/SAGE Wave 0) conducted in 2003 while the Chinese survey did not follow up the households from the earlier survey (Wave 0) in 2002. The samples were drawn using a stratified multistage cluster sampling in rural and urban areas of different provinces/states. The survey in China was conducted in eight provinces – Guangdong, Hubei, Jilin, Shaanxi, Shandong, Shanghai, Yunnan and Zhejiang. The survey in India was conducted in six states – Assam, Karnataka, Maharashtra,


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Rajasthan, Uttar Pradesh and West Bengal. For the purpose of this study, we restrict the sample to respondents aged 50 and above in either country. Our first research question is about how gender affects participation in paid and unpaid work among older adults in urban and rural China and India. We measure gender using a dummy variable where “1” indicates female and “0” indicates male. Paid work is based on participation for at least two days in the last seven days in any activities for which the respondent is paid in cash or kind or other activities involving selling things, small business, or work on the family farm or family business. Unpaid work is determined as participation in food preparation, housework, watching children and providing care to someone (which includes those outside home) in an average day. Our second research question is about the implications of such gendered participation in work on quality of life. Quality of life is measured using the World Health Organization Quality of Life scale, which has been validated cross-nationally, and has been modified for use in older subpopulations and successfully applied to aging research in Europe (Schmidt, Mühlan, & Power, 2006). The scale has eight items assessing an individual’s life quality across various domains such as physiology, economic security, psychology, and social relationship. We first reverse code the items so that higher values indicate better quality of life, then add up the values over the items, and rescale the total scores to 0 to 100. We control for a list of other individual and household characteristics that may influence how work affects quality of life or may directly influence quality of life. Age is grouped into three categories: 50–59, 60–69, and 70 and above. Physical condition is captured by the 12-item version of World Health Organization Disability Assessment Scheme (WHODAS II). WHODAS II assesses physical disabilities using a 1-5 Likert Scale to measure how much difficulty the respondent has doing activities of daily living and instrumental activities of daily living (Ustun et al., 2010). We take the sum over the 12 items and obtain an index ranging from 12 to 60. Household financial status is a major driving factor of “ceaseless toil” in old ages in developing countries (Benjamin, Brandt, & Fan, 2003; Davis-Friedman, 1991; Pang, Brauw, & Rozelle, 2004). We control for household financial status using a continuous measure of household assets (permanent income) provided by the WHO-SAGE. The measure was derived from the household ownership of durable goods, dwelling characteristics (type of floors, walls, and cooking stove), and access to services such as improved water, sanitation, and cooking fuel. Durable goods included number of chairs, tables, or cars, and if the household has electricity, a television, fixed and mobile phone, a bucket or washing machine and so on. Details on how the estimates were generated are reported by Arokiasamy et al. (2013:252). We use years of schooling to control for individual socioeconomic status and father’s education for family background. Father’s educational level is measured in three categories: no formal education, less than primary education, and primary education and above. Living arrangement may signify both the need for paid work and unpaid work and access to potential social support. For example, while living with a spouse may indicate availability of care and support for the respondent, it may also increase the burden of caregiving on the respondent when the spouse is in ill health. We use three dummy variables to indicate whether the respondent co-resides with spouse, children, and grandchildren respectively. Social involvement index is the predicted scores of factor analysis on a list of nine items asking about respondents’ involvement in the community. Some examples of the items include: attending any public meeting, socializing with coworkers outside of work, attending religious services, and so on. The information on participation in unpaid work was collected through the Day Reconstruction Module, which has four sets with different time frames (morning, afternoon, evening, and the whole day). A random quarter of respondents were assigned to each of the four


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sets. We use a categorical variable to control for the assignment of time frame. In addition, we include province dummies for China and state dummies for India in the analysis. ANALYTICAL STRATEGIES To address the first research question on how gender shapes participation in work among older adults, we run logistic regression models separately for urban China, rural China, urban India, and rural India using the following specification: (1) (2) where X includes the control variables introduced earlier. For the second research question on the implications of gendered work on quality of life, we conduct OLS regression models using the following specifications: (3) (4) Then we test if the effect of work differs by gender: (5)

To further understand how paid and unpaid work might mediate the effect of gender on quality of life, we perform mediation analysis using the potential outcomes framework (Imai, Keele, & Tingley, 2010; Imai, Keele, & Yamamoto, 2010). The strength of the 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 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. The mediation causal analysis model is specified below: (6) (7) To perform the mediation analysis, we use the user written command -medeff- in Stata 14. The codes make necessary adjustments for nonlinear models and report total, direct, indirect and ACMEs and their standard errors using a bootstrapping approach (MacKinnon, Fairchild, & Fritz, 2007; MacKinnon, Lockwood, & Williams, 2004). Bootstrapping is done by sampling with replacement, and a percentile based bootstrap confidence interval is created for inference purpose.


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RESULTS Gender Differences in Quality of Life Table 1 shows the descriptive statistics of the analytical sample by gender separately for urban China, rural China, urban India, and rural India. In all four societies, men report better quality of life than women. The gender difference in quality of life is smaller in urban China than in the other three societies. Among men, urban Chinese enjoy the highest quality of life, followed by urban Indians, rural Chinese, and rural Indians. Among women, urban Chinese report the highest quality of life and rural Indians the lowest, while rural Chinese and urban Indians report similar levels of quality of life. Gendered Nature of Work As shown by the different participation rates in paid and unpaid work among women and men in Table 1, work is highly gendered in all four societies. Urban Chinese have the lowest participation rates in paid work: 11% for women and 25% for men. In rural China, 53% of older women and 67% of older men are still engaged in paid work. The participation rates in paid work are also higher in rural India than in urban India. In rural India, 25% of older women and 64% of older men are involved in paid work, compared with 16% of older women and 55% of older men in urban India. There are two possible explanations for older urban Chinese’s low participation rates in paid work. On one hand, older urban Chinese enjoy greater access to pension than their counterparts in other societies, which enables them to afford not to do paid work. On the other, China’s national mandatory retirement ages (60 for men and 55 for women with some exceptions) apply to relatively more urban Chinese than rural Chinese, because the former are more likely to be employed in the formal sector where the retirement ages are enforced. Note that the gender differences in participation rates in paid work are larger in India than in China. As we discussed earlier, female labor force participation rates remained high in China despite the recent decline, as a legacy of the socialist state’s promotion of gender equality in the public sphere. Across societies, a great majority of older women (78-83%) are engaged in unpaid work, compared to less than half of older men. There is little societal variation in participation rates in unpaid work among women, but men’s participation rates vary considerably by societal context. While 45% of older men in urban China, 44% of older men in rural China, and 42% of older men in rural India perform unpaid work, only 32% of older men in urban India do so. The gender difference in participation rates in unpaid work is smallest in urban China. Moreover, among men, older urban Chinese have the highest participation rates in unpaid work. These patterns support our argument about lesser gender differences in division of labor in urban China than in the other three contexts. To better understand the gender patterns of work, we conduct additional analyses using a four-category variable to measure participation in work: “1” indicates participation in neither paid nor unpaid work; “2” participation in unpaid work only; “3” participation in paid work only; and “4” participation in both unpaid and paid work. The results show that, among women, rural Chinese have the highest participation rates in both paid and unpaid work (see Table A1 in the Appendix). These patterns indicate that the double burden on women is heavier in rural China than in the other three societies. More detailed analyses reveal that in rural China, as many as 45.6% of older women and 29.3% of older men are involved in not only paid work but also unpaid work. Rural China also has the largest difference between women’s and men’s participation rates in both unpaid and paid work at 16.3 percentage points. Note that it is only in rural China that more women than men are doing both unpaid and paid work. While 27% of rural Indian men and 16% of urban Indian men participate in both unpaid and paid work, 22% of rural Indian women and 13% of urban Indian


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women do so. Less than 9% of urban Chinese are doing both types of work, and the women do not differ much from the men in this regard. One explanation for the striking double burden on older rural Chinese women is the large-scale labor out-migration of able-bodied adults (predominantly men) and the consequent feminization of agricultural labor in rural China (Chang et al., 2011). These descriptive patterns are confirmed by the results in Table 2 based on logistic regression models. We are showing both odds ratios and average marginal effect to facilitate interpretation of the results. Consistent with our first hypothesis, the gender difference in the odds of doing paid work is smaller in rural and urban China than in India. In urban and rural China, the odds of doing paid work for older women are 62% (1-0.38) and 51% (1-0.49), respectively, smaller than that of older men. In urban and rural India, the odds of doing paid work for older women are 83% (1-0.17) smaller than that of older men. Based on the average marginal effects, the average probability of paid work for older women is 10-11 percentage points lower than that of older men in urban and rural China, in contrast to a difference of 27-31 percentage points in urban and rural India. The gendered patterns of unpaid work also support our first hypothesis. The odds of doing unpaid work for older urban Chinese women are 4.8 times larger than the odds for older urban Chinese men doing unpaid work. In rural China and urban and rural India, the odds of doing unpaid work for older women are 7.4, 8.8, and 8.4 times that of older men respectively. The average marginal effects show that the average probabilities of unpaid work are 28, 34, 38, and 39 percentage points higher for women than for men in urban China, rural China, urban India, and rural India, respectively. Implications of Gendered Work on Quality of Life Work not only is gendered to varying degrees across urban and rural China and India, but also has different implications on the quality of life of older women and men in these societies. In Table 3, we show the results of OLS regression models predicting quality of life for urban China, rural China, urban India, and rural India. Model 1 shows the overall effect of gender on quality of life, after controlling for age, individual physical disabilities, household financial status, other individual and family characteristics, and all other control variables. In Model 1, we observe statistically significant gender differences in quality of life in all societal contexts. Note that the magnitude of the gender effect on quality of life is rather modest. Interestingly, while women report lower quality of life than men in China, they report higher quality of life than men in India, all else being equal. We know from the descriptive statistics that women have lower quality of life than men in all contexts. In other words, the control variables are able to explain part of the female-male gap in quality of life in China. In India, when these individual characteristics, family background, and regional variation are taken into account, the sign of the gender coefficient even reversed. The results also show that regional variation across provinces/states, individual physical disabilities, household financial status, and age are the most important determinants of quality of life among older adults in China and India (full table available upon request). In Model 2, we add participation in work. The coefficients of gender are no longer statistically significant for urban and rural China, while the coefficients of gender become bigger in size for urban and rural India. This provides some evidence for the mediating role of work in gender differences in quality of life in China. Overall, the results suggest that paid work is more beneficial than unpaid work. Participation in paid work has a positive effect on quality of life in urban China and urban and rural India, but not in rural China. The beneficial effect of doing paid work only is larger in urban China than in India. However, participation in unpaid work has no statistically significant effect on quality of life in urban China or urban and rural India. In rural China, unpaid work is even negatively associated with quality of life.


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In Model 3, we add interaction terms between gender and work to examine if the effects of work differ by gender. The results suggest that the effect of work on quality of life does not differ between men and women in either urban China, or rural China, or urban India. The significant interaction term between gender and participation in paid work for rural India suggests that the effect of performing paid work is positive for older men but negative for older women. To identify the role of work in mediating the gender effect on quality of life, we present the results from causal mediation analyses in Table 4. Overall, relative to men, women suffer a loss in quality of life due to their less involvement in paid work in urban China and urban and rural India, but not in rural China. The size of average causal mediated effect of being female via paid work is larger in urban India than in rural India or urban China. Specifically, women’s less involvement in paid work contributes to 39% of their lower quality of life in urban China. Compared with older men, older women do not seem to gain from performing unpaid work in either urban China, or urban India, or rural India. Moreover, older rural Chinese women even experience a decrease in quality of life because of more involvement in unpaid work, compared with older rural Chinese men. Overall, older rural Chinese women’s greater performance of unpaid work contributes to 56% of their disadvantage in quality of life. To better understand these patterns, we estimate the average mediation effects of gender via paid and unpaid work for different subsamples by household financial status and individual physical limitations (see Table 5). The results show that, most evidently in India, older women of households with low financial status and older women with high levels of disability are disadvantaged by less involvement in paid work, relative to their male counterparts. Although overall there seems to be no female disadvantage associated with less involvement in paid work in rural China based on Table 4, Table 5 shows that among older adults with high levels of disability, women do suffer a loss in quality of life due to their lower participation rates in paid work. In contrast, among older urban Chinese with high levels of disability, less involvement in paid work does not put women at a disadvantage relative to men. This is probably because older urban Chinese with high levels of disability have better access to public support and are less dependent on paid work, compared to their counterparts in rural China and India. Hypotheses 3a and 3b are supported. Interestingly, the mediated effect of gender through less paid work among older urban Chinese with high household financial status and low levels of physical limitations are negative and statistically significant. In other words, well-to-do older urban Chinese women could have derived better quality of life were they engaged more in gainful employment. Performing unpaid work turns out to benefit women only among those with high levels of physical limitations. In urban and rural China and rural India, women with high levels of disability are able to translate their unpaid labor into some benefits in quality of life. Among older rural Chinese with high household financial status and low levels of disability, however, women suffer a loss in quality of life due to their greater burden of unpaid work, compared to men. Hypotheses 3c and 3d are only partially confirmed.


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DISCUSSION This paper is motivated by our concern about the gendered division of labor in later life and its implications for the wellbeing of older women and men in developing countries. China and India, the world’s most populous developing countries, are aging fast and both countries have a long tradition of patriarchal beliefs and practices that strongly discriminate against women. Due to women’s longer life expectancy than men in recent times, the aging population is disproportionately female. However, compared with older men, older women are living a longer life with fewer accumulated economic resources because of a lifetime’s experience of disadvantages in educational opportunities, employment opportunities, and inheritance rights. The social security systems are particularly weak in India and rural China with very few older individuals having access to adequate pension and health services. Out of economic necessity, older adults in India and rural China continue to work for as long as their physical conditions allow. While older men have greater access to opportunities of paid work, older women mainly rely on performing housework and care work for their family members in exchange for old-age support. Using data on older Chinese and Indian women and men aged 50 and above from the WHO Study on Global Ageing and Adult Health, we show that more older men than older women are involved in paid work and more older women than older men are performing unpaid work across different contexts. These patterns are consistent with the vast literature on gender division of labor, which reflects that the traditional gender norms of division of labor are deeply entrenched in our societies. The gendered division of labor favors men over women because our findings reveal that only paid work is positively associated with quality of life. We also find that the gender gaps in participation rates in paid work are smaller in China than in India, and older urban Chinese men are relatively more involved in unpaid work than older men in rural China, urban India, and rural India. We attribute the lesser gender differences in paid and unpaid work in China as a whole and urban China in particular to China’s decades-long high female labor force participation rate, which is a result of the state’s forceful intervention in promoting gender equity in the public sphere. The much lower levels of participation rates in paid work in urban China than in rural China, urban India, and rural India indicate that older adults may not want to perform paid work when they could afford choosing not to do so. We have discussed the role of economic necessity in older adults’ participation in paid work and the role of public provisions in making work more beneficial in greater details in another paper (Hu & Das, 2018). National mandatory retirement ages may be one reason to explain why so few urban Chinese are doing paid work, but we argue that the main explanation lies in the wider coverage of pension and hence lesser need to do paid work in old ages. This is because the retirement ages only apply to those with a formal job that provides old-age pension and other benefits. It is in urban China where paid work is least likely driven by economic needs that our results show that paid work is most beneficial. One implication of this finding is that older urban Chinese women could have gained higher quality of life by participation in paid work. Due to the massive labor migration of adult children from rural to urban China in pursuit of non-agricultural jobs, millions of older adults in rural China are left in the care of grandchildren and farmland. Unsurprisingly, the burden of housework and care work falls disproportionately on the shoulders of older rural women, in addition to their involvement in agricultural labor and other types of gainful employment. More detailed analyses of work reveal that older rural Chinese and especially older rural Chinese women face a double burden of both paid and unpaid work. High poverty rates, insufficient public provisions of healthcare and pension, shortage of labor in the household due to out-migration, and strong gender norms about division of labor all contribute to older rural women’s high participation rates in both paid and unpaid work. Unfortunately, paid work has no positive effect on quality of life, and unpaid work is even negatively associated with quality of life among


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older rural Chinese. As a result, we find that paid work does not mediate the effects of gender on quality of life in rural China, and rural Chinese women actually experience a decline in quality of life because of their involvement in unpaid work. These findings shed some light on the challenges older rural Chinese women are facing due to the socioeconomic and demographic transitions of China and the lack of sufficient social protection. Unlike the case of urban and rural China, urban India and rural India do not differ much from each other in how the relations between gender, work, and quality of life unfold. We therefore discuss the findings and implications for India as a whole. In both absolute and relative terms, the gender gaps in paid and unpaid work are larger in India than in China. By comparing India and China, we highlight the role of the state in increasing female labor force participation rates. We find that paid work is positively associated with quality of life among older Indians. Compared to older men, older Indian women have lower quality of life due to less involvement in paid work. This mediation effect is to a greater degree in India than in urban China. This greater gender inequality in quality of life due to paid work in India is more evident among the disadvantaged social groups with low household financial status and high levels of disability. On average, women do not seem to gain better quality of life from performing unpaid work in either India or urban China. In rural China, women’s greater involvement in unpaid work reduces their quality of life. The only exception is that, among those with high levels of disability in all three societies, women are able to exchange unpaid labor for better quality of life. In its current form there are two important limitations of the statistical analysis which is worth mentioning. It is very difficult to make a strong claim about causality from cross sectional data. However, in our opinion the observed associations are no less important for designing appropriate public policies. This limitation can only be overcome when later waves of this data is publicly available. In addition, in this study we felt that discussion of intensity and specific type of work is beyond the scope of this study. We believe that further research on these issues particularly for unpaid work will be highly informative for a fuller understanding of the problem at hand. As mentioned earlier, both countries in recent years have tried to push the responsibility of care for the elderly initially to the children and more recently, following paradigms in western welfare states facing growing burden of public provisions to the idea of productive aging (Butler & Gleason, 1985). Hailing economic productivity among older adults discriminates especially older women in three ways (Holstein, 1993). First, it obscures the fact that women have always been performing unpaid and under-appreciated labor including doing housework, caring, nurturing, and maintaining relationships. Second, it ignores the tenuous economic status of older women, which compels them to take whatever precarious and menial jobs available to them. Third, it fails to acknowledge and address the inherent gender and age biases of the labor market and risk helping to reproduce patterns of inequality. Focusing on the productivity of old age also disadvantages older adults with disabilities by narrowing their options in achieving a meaningful later life (Holstein, 1993). We conclude that both paid and unpaid work are less gendered and generate lower gender inequality in quality of life in urban China than in both rural China and India. Policy interventions should thus aim at promoting higher labor force participation among women and involvement in unpaid work among men and improving public support for adults in later life especially those with financial and physical limitations.


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Table 1: Descriptive Statistics of the Analytical Sample

Urban China Rural China Urban India Rural India

Women Men Women Men Women Men Women Men

Quality of life (mean) 65.62 67.39 62.55 65.25 62.38 66.51 59.15 62.01

(13.53) (13.30) (14.93) (14.57) (12.97) (13.20) (14.94) (14.69)

Paid work (%) 11.1 24.8 53.2 67.1 16.0 54.7 25.0 63.5

Unpaid work (%) 78.0 45.3 82.8 44.1 78.1 31.8 80.4 42.2

Age group (%)

50-59 42.4 38.9 47.5 47.6 47.7 44.2 49.2 42.3

60-69 28.3 29.2 30.4 31.5 35.1 34.3 34.0 35.4

70+ 29.3 31.8 22.1 20.9 17.2 21.4 16.8 22.3

Scores of WHO disability assessment schedule (mean)

15.14 14.60 16.69 15.41 23.43 20.32 25.56 22.40

(4.86) (4.55) (5.73) (5.16) (7.72) (7.62) (8.74) (8.39)

Index of household assets (mean) 0.13 0.15 -0.15 -0.13 1.00 1.02 0.64 0.66

(0.44) (0.43) (0.43) (0.42) (0.42) (0.41) (0.48) (0.48)

Spouse in the household (%) 70.8 85.7 72.7 81.4 61.1 89.8 63.4 87.6

Child in the household (%) 45.4 45.2 37 34.9 89.1 88.0 87.4 87.9

Grandchild in the household (%) 18.3 14.9 24.6 20.8 59.9 41.3 65.8 53.9

Index of social involvement (mean) -0.08 -0.07 0.04 0.15 -0.36 0.34 -0.29 0.36

(0.80) (0.78) (0.78) (0.84) (0.67) (0.97) (0.67) (0.94)

Years of schooling (mean) 6.76 8.95 2.40 4.69 4.12 8.27 1.07 4.52

(4.62) (4.14) (3.02) (3.39) (5.00) (5.23) (2.45) (4.76)

Father's education (%)

No formal education 56 50.9 82.6 80.1 44.8 44.8 75.0 71.4

Less than primary 26.9 30 15 17 30.3 35.2 17.9 23.4

Primary and above 17.1 19.1 2.5 2.9 24.9 20.0 7.1 5.2

Observations (N) 2,758 2,258 2,850 2,896 686 676 1,886 2,211

Standard deviations in parentheses


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Table 2: Results of Logistic Regression Models Predicting Paid Work and Unpaid Work


Odds Ratio

Average Marginal

Effect

Odds Ratio

Average Marginal

Effect

Odds Ratio

Average Marginal

Effect

Odds Ratio

Average Marginal

Effect

Paid work

Female 0.38*** -0.11*** 0.49*** -0.10*** 0.17*** -0.27*** 0.17*** -0.31***

(0.04) (0.01) (0.04) (0.01) (0.04) (0.03) (0.02) (0.02)

Unpaid work

Female 4.78*** 0.28*** 7.40*** 0.34*** 8.81*** 0.38*** 8.35*** 0.39***

(0.35) (0.01) (0.53) (0.01) (1.59) (0.02) (0.85) (0.01)

Note: All models control for household wealth index, WHO disability index, 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, 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<0.001, ** p<0.01, * p<0.05, ^ p<0.1


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Table 3: Results of OLS Regression Models Predicting Quality of Life


Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3

Female -0.72* -0.47 -0.46 -0.72* -0.32 -0.87 1.18^ 1.76* 0.81 1.36** 1.72*** 2.77***

(0.34) (0.37) (0.63) (0.34) (0.38) (0.78) (0.67) (0.75) (1.18) (0.42) (0.47) (0.79)

Paid work 2.76*** 2.88*** -0.09 -0.10 1.43* 1.10 0.81* 1.56**

(0.47) (0.59) (0.41) (0.54) (0.66) (0.80) (0.40) (0.51)

Unpaid work 0.36 0.34 -1.10** -1.37** -0.22 -0.72 -0.21 -0.00

(0.38) (0.49) (0.38) (0.46) (0.63) (0.82) (0.39) (0.47)

Female* Paid work

-0.29 0.01 0.91 -1.79*

(0.88) (0.66) (1.25) (0.76)

Female* Unpaid work

0.06 0.79 1.08 -0.48

(0.73) (0.79) (1.27) (0.79)

Constant 78.98*** 77.51*** 77.48*** 83.18*** 84.21*** 84.27*** 78.53*** 77.29*** 77.71*** 81.52*** 80.75*** 80.03***

(1.10) (1.15) (1.17) (0.92) (1.05) (1.09) (1.75) (1.93) (1.97) (1.02) (1.14) (1.17)

R-squared 0.296 0.301 0.301 0.364 0.365 0.366 0.47 0.472 0.472 0.482 0.483 0.483

Observations 5,016 5,016 5,016 5,746 5,746 5,746 1,362 1,362 1,362 4,097 4,097 4,097

Note: All models control for household wealth index, WHO disability index, 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, province or state dummies, and assignment of set A, B, C, or D of the Day Reconstruction Module. Robust standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05, ^ p<0.1


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Table 4: Mediated Effects of Gender (Male=Reference Group) on Quality of Life via Paid Work and Unpaid Work

Average causal mediation effect

Average direct effect

Total effect

% of total effect mediated

Via paid work

Urban China -0.3 (-0.43,-0.19) -0.45 (-1.2,0.29) -0.75 (-1.5,-0.03) 39.85 (17.15,221.86)

Rural China 0.01 (-0.07,0.09) -0.29 (-1.05,0.45) -0.29 (-1.03,0.45) -1.59 (-22.53,16.56)

Urban India -0.44 (-0.88,-0.05) 1.79 (0.26,3.34) 1.34 (-0.19,2.78) -31.27 (-242.49,114.82)

Rural India -0.29 (-0.58,-0.01) 1.78 (0.84,2.74) 1.49 (0.56,2.37) -19.6 (-51.74,-12.3)

Via unpaid work

Urban China 0.12 (-0.09,0.35) -0.45 (-1.2,0.29) -0.33 (-1.03,0.35) -24.88 (-232.71,542.74)

Rural China -0.4 (-0.67,-0.12) -0.29 (-1.05,0.45) -0.7 (-1.36,-0.02) 56.17 (25.11,351.49)

Urban India -0.1 (-0.62,0.49) 1.79 (0.26,3.34) 1.69 (0.24,3.1) -5.85 (-23.28,-3.06)

Rural India -0.09 (-0.39,0.25) 1.78 (0.84,2.74) 1.69 (0.8,2.57) -5.13 (-10.81,-3.39)

95% Confidence Interval in parentheses


20

Table 5: Average Causal Mediation Effect of Gender (Male=Reference Group) via Paid and Unpaid Work by Household Financial Status and Individual Physical Limitations


Via paid work

Full sample -0.3 (-0.43,-0.19) 0.01 (-0.07,0.09) -0.44 (-0.88,-0.05) -0.29 (-0.58,-0.01)

Subsample by household financial status

Low -0.17 (-0.34,-0.04) 0.03 (-0.08,0.14) -0.39 (-1.36,0.41) -0.41 (-0.75,-0.08)

High -0.42 (-0.61,-0.24) -0.06 (-0.23,0.1) -0.41 (-0.91,0.07) 0.04 (-0.41,0.51)

Subsample by individual physical limitations

Low -0.51 (-0.73,-0.32) 0.1 (-0.06,0.25) -0.34 (-0.93,0.21) -0.44 (-0.97,0.09)

High -0.09 (-0.22,0.01) -0.27 (-0.43,-0.15) -1.29 (-2.18,-0.54) -0.73 (-1.09,-0.38)

Via unpaid work

Full sample 0.12 (-0.09,0.35) -0.4 (-0.67,-0.12) -0.1 (-0.62,0.49) -0.09 (-0.39,0.25)

Subsample by household financial status

Low 0.38 (-0.02,0.84) -0.31 (-0.62,0.03) -0.06 (-1.43,1.17) -0.04 (-0.48,0.38)

High -0.09 (-0.36,0.21) -0.63 (-1.14,-0.06) -0.22 (-0.88,0.4) -0.29 (-0.77,0.18)

Subsample by individual physical limitations

Low -0.12 (-0.38,0.17) -0.93 (-1.35,-0.47) -0.82 (-1.65,-0.06) -0.2 (-0.68,0.24)

High 1.13 (0.72,1.61) 0.71 (0.34,1.12) 0.77 (-0.19,1.71) 0.87 (0.36,1.37)

95% Confidence Interval in parentheses. Bold font indicates statistical significance at 0.05 level.


21

Table A1: Distribution of Participation in Work by Gender and Societal Context

Participation in work (%) Urban China Rural China Urban India Rural India

Women Men Women Men Women Men Women Men

None 19.43 38.88 9.58 18.02 18.8 29.73 16.17 21.48

Unpaid work only 69.51 36.27 37.23 14.85 65.16 15.53 58.85 15.06

Paid work only 2.61 15.85 7.58 37.85 3.06 38.46 3.45 36.32

Both unpaid and paid work 8.45 8.99 45.61 29.28 12.97 16.27 21.53 27.14

Observations (N) 2,758 2,258 2,850 2,896 686 676 1,886 2,211

work in old age and quality of life: gender divide in ... · within nus it works particularly with...

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