effects associated with marital status transition among elders in China

Yu Sun, Virginia Tech, [email protected]

Wen You, Virginia Tech, [email protected]

Selected Paper prepared for presentation at the 2018 Agricultural & Applied Economics Association Annual Meeting, Washington, D.C., August 5-August 7

Copyright 2018 by [Yu Sun, Wen You.]. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.

1. Introduction

Elderly people’s health is determined not only by genetic factors, but also by nutritional and socioeconomic factors (Lindeboom, Portrait, and van den Berg 2002). Marital status is shown to be an essential determinant of health (Zella 2017; Kiecolt- Glaser and Newton 2001). has a protective effect on the elders’ health through daily care, financial and spiritual support (Zhou and Hearst 2016b). In addition, the transition of marital status have a particularly great impact on the health of older individuals. The loss of a spouse increases greatly the risk of depression or anxiety (Fried 2015; Turvey et al. 1999). If the martial status transition has a large effect on health among the aged people, the transition may be a useful signal for the health care workers to provide special health service on the occurrence of such an event. Insight in the effect of marital status transitions on health among the elder people is vital to monitor future needs of health care for the elders and carry out prevention of relevant . This issue is especially vital to the Chinese health policy designers as rapid population aging and high divorce rate are two of the most pressing challenges in China.

Rapid population aging has brought up not only challenges of health service and care but also rising economic burdens in China. The demands for health care for the aged individuals are determined by the distribution of health status among the elder population. According to the Social Service Development Statistical Bulletin, the proportion of population aged 60 and over has risen up to 16.7% of the total population by the end of 2016 (Ministry of Civil Affairs of the People's Republic of China 2017). The prevalence rate of chronic diseases among Chinese elders has been increasing dramatically from 50.1% in 2003 to 71.8% in 2013 (Department of Family Development 2016). The 13th Five-Year Plan of Healthy Aging in China highlights the needs of systematic research on understanding the effects of various factors on elder health to address the widespread concerns of healthy aging (The State Council 2017). Studies investigating the effects of marital status on health among Chinese elders are relatively rare in the current literature. Changes in marriage culture and patterns have led to decreasing marriage rate and rising divorce rate. According to data from Population Census of the People’s Republic of China, the number of divorced population aged 60 and older has increased from 0.79 million in 1990 to 1.38 million in 2010 (Sun 2015). Hence, there is an urgent need to explore the health effects of marital status transition among elderly population in China in order to promote the health of vulnerable elders by meeting their medical and economic needs.

The purpose of this study is to further investigate the effects of marital status transition on health outcomes among Chinese elders. We will take into account the sequencing of marital statuses instead of current or initial marital status to provide a further understanding of how marital status transition affect health among elders. One problem with current marital status is that it does not consider the marital event sequence an individual has experienced (Burman and Margolin 1992). Specifically, stably married and one exit followed by remarriage can both lead to a current marital status of being married. Failing to consider the marital history may lead to biased conclusion that those drastically different marital history has the same effects on health outcomes. Following the labor economics literature, we hypothesize that marital transitions from married to divorce or to widowhood have negative effect on health among Chinese elders and much larger than other marital status sequences. In addition, marriage duration is positively associated with health, whereas the duration of divorce and widowhood has negative effects. Uncovering these associations is essential to better understand subsequent morbidity and mortality to assess the various needs of health care for elders. Furthermore, our study will provide information for pinpointing vulnerable elders who suffer more from the marital transitions with unmet needs of support from family and social security. Public health policies including psychological services, medical care and financial support should be provided with special attention to those population. We also investigate the discrepancy in the effects of the marital transition.

2. Literature on marriage and health

Several theories and models are proposed to explain the association between health outcomes and marriage, including the stress/ model (Berkman 1984), marriage protection theory (Umberson 1992; Ross, Mirowsky, and Goldsteen 1990; Va et al. 2011) and marital resource model (Gary 1981; Liu and Umberson 2008; Liu 2012) . The basic ideas are similar and indicate that social relationship is essential to health outcomes. In particular, marriage has a protective effect on health due to improved social support (Mirowsky and Ross 1989; Schieman, Van Gundy, and Taylor 2002), greater economic resources (Waite 2009; Mandara et al. 2010; Vespa and Painter 2011)and shift to healthy lifestyle (Schone and Weinick 1998; Umberson, Crosnoe, and Reczek 2010). A range of studies show support of the protective effect of marriage on health. For instance, married patients were associated with healthier dietary behaviors in a study examining the marital status differences in management of a chronic (August and Sorkin 2010). Similarly, marital dissolution has detrimental effects on health. Less social support and reduced economic resource due to divorce or widowhood may lead to poor health among the divorced or widowed (Waite and Gallagher 2000). Those predictions are in line with the crisis model, suggesting that the negative effects on health will be alleviated overtime (Thierry 1999).

A more recent research stream indicates that researchers begin to analyze the relationship between marriage and health from a life course perspective and take into account of the marital trajectories. The main reason for those emerging literature is that the association of marital status and health may vary in response to changes in some individual characteristics. By including different components of marriage, the knowledge of the effects of marriage on health will be advanced (Dupre and Meadows 2007). Essential elements of marital trajectory consist of marital sequencing, timing, transitions and duration (Dupre and Meadows 2007). Transitions have been examined in the majority of current literature, including the effects of transition into marriage (marital formation) and out of marriage (marital dissolution). Kalmjin reports that more studies have investigated the effect of marriage exit on health than that of marriage entry and a majority of these studies show supports for marital protective effects (Kalmijn 2017). However, Western are different from Chinese marriages in some aspects, such as conjugal bonds are stronger in Western marriages (Pimentel 1994; Chen et al. 2015). An investigation in a non-Western setting is needed to illuminate the relationship between marriage and health.

Some studies have explored the relationship between marriage and health in the aging literature by using static marital status. Va et al. investigate the association between marriage and health among middle age and elderly residents in China. Their results show that being married reduces all-cause mortality and being divorced or widowed increases mortality risks (Va et al. 2011). However, this study focuses on marriage and mortality by using marital status at baseline and is limited to a sample of permanent residents in Shanghai. Another study based on samples from three provinces (Jiangsu, Henan and Qinghai) find that widowed individuals aged 60 and over in rural areas have lower quality of life relative to the married counterparts (Zhou and Hearst 2016a). Moreover, most of past studies utilize cross-sectional analyses of marriage and health outcomes among elder adults. Far fewer studies has focused on the effect of marital transition on elder health in China. The present study will provide a dynamic analysis of whether the marital transition have effects on elder health by using China Health and Retirement Longitudinal Study (CHARLS).

3. Empirical model specification

To make a causal inference about the effect of marital transition on health status, one could utilize the following basic model:

Hi01  MT i  u i (1)

where Hi is the health status of individual i, the MTi is the marital transition for individual i. The ui term includes individual unobservable characteristics. If marital transition (MTi) is uncorrelated with unobservable characteristics ui, OLS estimation of this simple model will give rise to an unbiased and consistent estimator. Unfortunately, this model has to deal with a major econometric challenge because health status also affects the decision to stay in marriage or not. That is to say the marital transition MTi may be endogenous due to unobserved characteristics. For instance, it is likely that unobserved characteristics like personality and temperament may lead to endogeneity of martial transition. Therefore, cross-sectional estimation by OLS is probably to yield a biased estimate of coefficient of marital transition. Fixed-effects strategy could solve this potential endogeneity since most of the unobserved factors are unlikely to change much over time. CHARLS is an ongoing longitudinal study and it allows us to control for the individual unobservable characteristics that could give rise to endogeneity. Random-effect model is specified as follows:

Hit0   1 MT it   2 X it   i  u it (2)

where i indicates individuals, t indicates time (2011, 2013 and 2015); Hit is the health

status of individual i at time t; X is a vector of control variables; i is the unobserved

characteristics that are time-invariant; uit is the error term. The health outcome of interest is a binary variable and a random-effects logit model is used to exploit the longitudinal feature of the dataset.

4. Data

CHARLS has a representative sample of persons aged 45 years and older in China and collects rich information of social, economic factors and health outcomes of community residents. It has conducted a national survey biannually since 2011. The survey includes about 10000 households from 28 provinces. Our empirical analysis covers three waves of CHALS (2011, 2013, and 2015) and focuses on a subsample of respondents aged 60 and over in urban and rural China.

Health outcome of interest is measured by self-reported health status (SRHS) in the survey which has been shown to be a reliable measure of physical health (Miilunpalo et al. 1997) and a potent predictor for morbidity and mortality (Hu et al. 2016; Feng et al. 2016). In addition, a range of studies show that self-reported health status is valid to measure general health among various population (Gray et al. 2012). In CHARLS, the health status is recorded according to the following question: “Would you say your health is excellent, very good, good, fair, or poor?” SRHS is recoded as a dummy variable with one indicating excellent, very good and good health and zero indicating fair and poor health.

The key independent variable in this analysis is a dynamic measure of marital status: marital transition. Based on a study by Dupre et.al. (2007), we utilize sequencing of marital status to measure marital transition (Dupre and Meadows 2007). Marital transition is indicated by a categorical variable by comparing the marital status at baseline and that at follow-up: stably married, transition to divorce and transition to widowed hood. Duration is defined as the number of waves in each marital status by using a categorical variable which is defined as: married in three waves (reference), transition to divorce in one wave, transition to divorce for more than one wave, transition to widowhood in one wave, and transition to widowhood for more than one wave.

Other control variables include demographic covariates, socioeconomic covariates and behavioral factors. Demographic characteristics include age, quadratic term of age. Socioeconomic variables include education (categorized into four groups: no formal education, elementary school, high school and college or above), indicator of rural area (rural vs. urban areas) and individual income. Individual income is a combination of the following sources: individual wage income, individual self-employment income, agricultural income, pension income, transfer income, and net asset income (Yaohui et al. 2015).We also use household expenditure per month to measure the economic resources as a robust check. Past literature show that household expenditure is more accurate than per capita household income to indicated economic status for the elderly (Smith, Majmundar, and Council 2012; Strauss et al. 2010). We also include behavior factors, such as whether the respondent reporting smoking or not. In addition, we use a dummy variable to indicate whether a participant is insured or not. Insurance plan include New Rural Cooperative Medical Scheme (NCMS), the Urban Resident Medical Insurance (URMI) and Urban Employee Medical Insurance (UEMI) and others.

5. Results

Descriptive statistics of our sample in this analysis are presented in Table1. The distribution of self-reported health status is slightly different among respondents with different marital status. In each subgroup, the majority report “fair” health (37%-41%). Compared to married respondents, the distributions of health status shift toward “poor” among respondents with other type of marital status (Figure 1).

Health distribution among subgroups with different marital status Married Separated/Divorced

1

.5

0 Widowed Never married

Density

1

.5

0 Excellent Very good Good Fair Poor Excellent Very good Good Fair Poor Marital Status

Figure 1. The distribution of health status among subgroups with different marital status (2011, 2013 and 2015)

Average age of the whole sample is about 69 years old, while widowed participants are about 5 years older. The majority of the widowed are women with a higher proportion of female than the overall sample (73% vs. 50%). Married and separated/divorced individuals have higher education and higher individual income than the never-married as well as the widowed. About 40% of the sample are from rural village. Most of our sample are covered with some kind of health insurance (97%-99%). Less than 24% of the married respondents report smoking and about 40% of the separated/divorced individuals reported smoking.

Table 1. Descriptive statistics. (Population age ≥ 60 years) Variable Overall Never Married Separated/ Widowed sample married divorced n=24,614 n=217 n=19,427 n=245 n=4,724 Self-reported health status (%) Excellent 0.04 0.02 0.04 0.02 0.04 Very good 0.10 0.06 0.10 0.12 0.10 Good 0.30 0.21 0.31 0.27 0.28 Fair 0.37 0.39 0.37 0.41 0.37 Poor 0.19 0.31 0.18 0.17 0.22

Age, mean (SD) 68.56 67.85 67.32 68.57 73.51 (7.10) (6.83) (6.19) (7.85) (8.21)

Gender (%) Female 0.50 0.06 0.45 0.38 0.73

Highest level of education (%) ≤ Elementary school 0.80 0.96 0.78 0.74 0.89 Middle school 0.12 0.04 0.14 0.15 0.07 High school/Prof/Tech 0.06 0.00 0.07 0.08 0.03 ≥ College 0.01 0.00 0.01 0.03 0.00

Region (%) Rural village 0.39 0.52 0.39 0.29 0.40

Individual income, mean 5,250.29 2,115.83 5,699.03 6,319.92 3,549.61 (SD) (10,926.35) (5,232.61) (11,404.82) (11,986.59_ (8,694.04)

Health Insurance (%) Insured 0.99 0.97 0.99 0.98 0.98

Health behavioral factor Ever smoking (%) 0.23 0.47 0.24 0.40 0.15

Time effect (%) 2011 30.17 0.94 77.84 1.3 19.91 2013 30.73 0.86 78.6 0.91 19.64 2015 39.10 0.94 78.62 0.83 19.62

We begin with the full sample, and then we conduct analysis for females and males respectively. Table 2 shows the estimation results of the overall sample with the results of Logit model presented in the second column and those with random-effect specification presented in the third column. The standard errors are adjusted for clustering at the household level. Compared to married respondents, those who never get married have a lower probability of reporting good health. However, there is no significant difference in the likelihood of reporting good health between the married respondents and those who are separated/divorced or widowed.

Table 2. Regression estimation of overall marginal effects (Average marginal effects)

Variables Logit model Random effects logit model Age -0.05*** -0.24*** (0.01) (0.06) Squared age 0.00*** 0.00*** (0.00) (0.00) Female -0.05*** -0.28*** (0.01) (0.07) Middle school 0.04*** 0.23*** (0.02) (0.08) High school/Prof/Tech 0.05** 0.29** (0.02) (0.12) ≥ College 0.03 0.17 (0.05) (0.26) Rural village -0.06*** -0.29*** (0.02) (0.08) Individual income 0.00*** 0.00*** (0.00) (0.00) Insured 0.06 0.34* (0.04) (0.19) Ever smoking -0.04** -0.22** (0.02) (0.09) Separated/ 0.02 0.00 divorced (0.06) (0.29) Widowed -0.00 -0.04 (0.01) (0.07) Never married -0.16** -0.71* (0.08) (0.42) 2013 0.03** 0.15* (0.02) (0.09) 2015 0.10*** 0.46*** (0.02) (0.11) *** p<0.01, ** p<0.05, * p<0.1

We undertook an exploration of the gender differences in the effects of marital status on health. Table 3 presents the estimation results for females and males using Logit model and random effect logit model separately. The results show that never-married females are less likely to report good health compared to those who are married. The difference between married and never-married are not significant for men while the widowed men are significantly less likely to report good health relative to who married.

Table 3. Regression estimation of overall marginal effects for females and males (Average marginal effects) Variables Females Males Logit model Random Logit model Random effects logit effects logit model model Age -0.03** -0.18*** -0.09*** -0.50*** (0.01) (0.07) (0.03) (0.14) Squared age 0.00** 0.00** 0.00*** 0.00*** (0.00) (0.00) (0.00) (0.00) Middle school 0.03 0.14 0.06** 0.36** (0.02) (0.11) (0.03) (0.15) High 0.05* 0.31** 0.05 0.28 school/Prof/Tech (0.03) (0.15) (0.04) (0.19) ≥ College 0.08 0.50 0.01 0.02 (0.09) (0.40) (0.07) (0.37) Rural village -0.05*** -0.23** -0.10*** -0.57*** (0.02) (0.09) (0.03) (0.18) Individual 0.00*** 0.00*** 0.00*** 0.00*** income (0.00) (0.00) (0.00) (0.00) Insured 0.05 0.28 0.12 0.71 (0.04) (0.21) (0.09) (0.46) Ever smoking -0.00 -0.01 -0.06** -0.33*** (0.04) (0.18) (0.02) (0.12) Separated/ 0.04 0.11 -0.06 -0.32 divorced (0.06) (0.35) (0.10) (0.55) Widowed -0.00 -0.02 -0.08** -0.47** (0.01) (0.07) (0.04) (0.20) Never married -0.45* -1.99* -0.11 -0.49 (0.27) (1.20) (0.08) (0.48) 2013 0.04 0.17 0.03 0.12 (0.02) (0.11) (0.03) (0.16) 2015 0.10*** 0.45*** 0.12*** 0.59*** (0.03) (0.13) (0.04) (0.21) *** p<0.01, ** p<0.05, * p<0.1

6. Discussion

Marital relationship is one of the most important relationships and is proved to affect health by a majority of studies (Zella 2017; Williams and Umberson 2004; Waldron, Hughes, and Brooks 1996). Most of these studies focus on the Western-style marital relationship and indicate that marriage has a protective effect on health through increasing economic resources (Dupre, Beck, and Meadows 2009), buffering stress (Schwerdtfeger and Friedrichmai 2009), and improving health-related behavior (Broman 1993; Umberson 1992). The purpose of this study is to examine whether the protective effects are applicable to Chinese elders since conjugal bond is weaker relative to intergenerational ties in China.

We examine the question using the data from CHARLS 2011-2015 surveys. A panel logit model for the binary health variable with random effect specification is utilized to estimate the effects of marital status on health among Chinese elders. We find that the never-married respondent are less likely to report good health than those who are married. From the subgroup analysis, the results suggest gender differences in the effect of marital status on self-reported health status. Widowed males are less likely to report good health compared to married respondents while never-married females have significant lower possibility to report good health than those married. However, we cannot rule out the possibility that the marital transition is caused by changes in health outcomes due to the lack of timing of marital transition and incidence of a disease. The evidence for gender differences is consistent with previous studies. Several studies have also found that the marital status affect men and women differently (Va et al. 2011; Dupre and Meadows 2007).

Our findings suggest that respondents who never being married are significantly less likely to report good health than respondents who are married and lend some supports to the protective effects of marriage. In addition, the lack a marital relationship may pose some environmental stress to those who are never married (Burman and Margolin 1992). Hence, what and how alternative supports outside the marital relationship would help those people to live health lives should be examined to develop effective public health policies to improve the health of never-married elders. Furthermore, the subgroup analysis reveal that widowed men have lower possibility to report good health compared to those who are married. The proportion of widowed respondents is around 20% in our sample. As the aging population is expected to grow in the future, there will be more individuals who become widowed. Previous studies show that the mortality risk is higher within the short period after losing a spouse (Martikainen and Valkonen 1996). Health policies, such as intervention programs, health services or social support, should be timely delivered to the vulnerable population with an attention to the widowed men.

Our study is based on a nationally-representative population and a large sample size. In addition, the present analysis utilizes the longitudinal feature to examine the causal effects of marital transition on health. However, there are some limitations in this study. First, several important factors of marital history are absent in the survey and could mot be examined. For instance, we are unable to the age of first marriage in the analysis because much information of first marriage timing is missing in the data. Marriage quality is not measured in the survey, which is salient to health outcomes. Furthermore, the influence of potential measurement error is not excluded.

This study will improve our understanding of how to develop efficient methods of allocating limited resources to meet the medical and economic needs among Chinese elders. One feature of our study is that we investigate the effects of sequencing of marital status instead of current marital status on health outcomes. The preliminary results show that never-married elders and elders with marital exit to widowhood or divorce are less likely to report good health status relative to married elders. A range of strategies are in need to deal with caring the widowed and never-married elders in the coming decades. Both family and social support should be provided to the elders without spouses. Health insurance, health services and financial assistance should be extended to allow elder population to migrate to live with their adult children to receive health care from family. Specifically, future policies should enable the reimbursement system of medical care from focusing on place of residence to trans-province.

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