Your Sadness, My Sorrow: Negative Emotion Concordance Within Married Couples in China 1. Introduction

Your Sadness, My Sorrow: Negative Emotion Concordance within Married Couples in China

Wanru Xiong Office of Population Research Princeton University

Abstract: This paper finds significant evidence for negative emotion concordance within married couples age 45 and above in China using CHARLS data. To understand the mechanism of such concordance and test the hypothesis of emotional contagion effect, I applied instrument variable method, structure equation model and fixed effect model in this dyadic relation context. The results confirm the effect of spouse’s negative emotion on the respondent’s and support the emotional contagion effect hypothesis. In general, wives tend to be more sensitive to the emotional condition of their husband. Keywords: dyadic relation; negative emotion; concordance; emotional contagion effect

1. Introduction

“I will laugh with you and cry with you ... in sickness and in health, till death do us apart.” Couples mean it when they take their wedding vows. There is evidence of health concordance within married couples, which means couples’ health conditions, both physical and mental, tend to converge after marriage (Meyler, Stimpson and Peek, 2007). This phenomenon of health concordance within couples is drawing increasing research interests. Published findings of physical health concordance within couples include concordance in blood pressure (Beresford, 1976; Speers et al., 1986; Peek and Markides, 2003; Stimpson and Peek, 2005), chronic conditions (Cheraskin et al., 1968; Bookwala and Schulz, 1996; Ubeda, 1998; Stimpson and Peek, 2005) and obesity (Inoue et al., 1996; Knuiman et al., 1996). Among the growing number of papers on health concordance within couples, studies on depressive symptoms and distress are the most prevalent (Meyler, Stimpson and Peek, 2007). According to the comprehensive literature review by Meyler, Stimpson, and Peek (2007), there is overwhelming evidence for concordance of depressive symptoms and distress within couples (Tower and Kasl, 1995; Siegel et al., 2004; Kouros and Cummings, 2010). Nevertheless, there are still debates about the underlying mechanisms, among which emotional contagion is a candidate hypothesis. By far, none hypothesis has won the consensus among researchers (Meyler, Stimpson and Peek, 2007). Other researchers focusing on ethnicity and cultural factors in depression concordance reveal ethnic and cultural difference (Peek et al., 2006). However, more than half of these findings are based on small-scale clinical samples, therefore may be subject to sample selection bias and sample size limitations. Besides, as far as I know, there is no published research on this topic in China. This paper aims to answer the following questions: Are there depression concordance within middle-aged or elderly married couples in China? Is such concordance the result of emotional contagion? If it is, is there a gender difference in the contagion effect? In this paper, I use “China Health and Retirement Longitudinal Study (CHARLS)” data to study depression contagion within married couples aged 45 1

and above. I first review and summarize sociological and psychological literature about the empirical findings and the potential theoretical mechanisms of depression concordance within couples, with emphasis on emotional contagion hypothesis. Then I document the depression concordance phenomena using both cross-sectional and panel data from CHARLS. To further investigate these phenomena, I use instrumental variable (IV) methods, structural equation model (SEM) and fixed effect model to identify the emotional contagion effect in this dyadic relation context. I find that depression concordance within married couples aged above 45 in China is significant. The models support the hypothesis of emotional contagion and show gender differences in such contagion effect. The rest of the paper is organized as follows. Section 2 reviews the empirical findings and theoretical hypotheses from related literature. Section 3 gives details on sample data and variables. Section 4 shows evidence for depression concordance within spouse. Section 5 sets up a contagion model and tests the emotional contagion hypothesis. Empirical findings are presented along with their interpretations. Section 6 finishes with discussions and concluding remarks.

2. Depression concordance within couples

2.1. Depression concordance Psychologist and sociologist have reported evidence of mental health concordance within couples. Among 58 published papers on mental health concordance articles reviewed by Meyler et al. (2007), only three studies did not find concordance. Evidence of depression concordance is particularly strong. Though concordance involves both parties, wives’ mental health tends to be more “relationship related” than that of their husbands (Meyler, Stimpson, and Peek, 2007; Moffitt, Spence and Goldney, 1986; Peek et al., 2006). The explanation for depression concordance is still unclear, though emotional contagion hypothesis is more frequently suggested. Among mental health concordance literature, most studies rely on cross-sectional clinical data, and half of them have sample size less than 100 (Meyler, Stimpson and Peek, 2007). Before regression analysis is applied, earlier researchers use variance analysis and t-test. The Recent development of structural equation model is gaining attention from concordance researchers (Meyler, Stimpson and Peek, 2007). Some researchers regard it as a necessary tool to deal with the simultaneous process of mutual convergence (Peek et al., 2006). 2.2. Possible mechanisms and hypotheses There are four major theories explaining health concordance within couples. All of them fit into the context of depression concordance. According to the comprehensive review by Meyler et al. (2007), few studies have explicitly tested these theories, mainly due to insufficient data. 2.2.1. Assortative mating Assortative mating is the term used to describe “any systematic departure from random mating” (Vandenberg, 1972. pp.2) Usually, individuals are more likely to marry someone who is similar to themselves, such as in age (Rele, 1965; Lutz, 1918), ethnic group (Kennedy, 1944), education (Lewis and Oppenheimer, 2000) and socioeconomic status (Kalmijn, 1994).

There are two potential ways in which assortative mating would lead to depression concordance within couples. First, if assortative mating happens concerning health and mental health in particular, then individuals who are vulnerable to depression are likely to marry a similar spouse. As a result, in survey data, we might observe depression concordance. The other case in which assortative mating might play a role happens when depression is related to factors such as ethnicity and education. To illustrate, assume that Ph.D. students are more vulnerable to depression while they tend to marry another Ph.D., then we would observe highly concordant depression within Ph.D. couples. Empirically it is hard to identify the above direct and indirect effect of assortative mating. Several studies using longitudinal data supported the assortative mating hypothesis (Eagles et al., 1987; Siegle et al., 2004). The idea in this paper is that if there is evidence that depression concordance varies with the increase of marital duration, then we could treat assortative mating as fixed effect and test other hypotheses in the process of depression concordance. 2.2.2. Shared resource hypothesis While assortative mating hypothesis emphasizes on the pre-marriage condition, shared resource hypothesis focuses on the shared environment and experiences after marriage. After their wedding, married couples might go through ups and downs, through rich and poor, instead of living happily ever after. All these experiences would translate into shared health risk and fuses for negative emotion (Rose, Mirowsky, and Goldsteen, 1990; Smith and Zick, 1994; Waite, 1995). One difficulty in testing this hypothesis is that the relations between married couples are so close that it 's hard to tell whether an event is private or shared. 2.2.3. Caregiving Caregiving might not be pleasant. According to Schulz et al. (2001), there is clear evidence that providing informal care can lead to negative emotion and detrimental physical health effects on elderly spousal caregivers. Even stronger effect is found among intense caregivers (Majerovitz, 1995; Sansoni et al. 2004) and female spousal caregivers (Harwood et al., 1998; Thompson et al., 2004) Given there is connection between one spouse’s health condition and the other’s mood, adding that one’s happiness is positively related to physical health condition, we would probably notice depression concordance within couples via the mechanism of caregiving effect. 2.2.4. Emotion contagion Emotions are contagious (Darwin, 1872; Hatfield, Cacioppo and Rapson, 1994; Joiner, 1994; Katz, Beach and Joiner, 1999; Barsade, 2002). The metaphor “you are my sunshine” well catches the essence of happiness eradiation. Psychologists refer to the phenomenon that one individual's mood converges to the other's after exposure to his/her mood state as emotional contagion (Gonzales, Castillo, and Swinkels, 2002; Hatfield, Cacioppo and Rapson, 1994). Hatfield et al. (1994) pointed out that emotional contagion is multilevel: “The precipitating stimuli arise from one individual, act upon one or more other individuals, and yield corresponding or complementary emotions.” (pp.5) The emotional and behavioral synchrony as the result of emotional contagion has adaptive utility for the social relationship (Hatfield, Cacioppo and Rapson, 1994). We can see that emotional contagion is intrinsically a social network issue. Totterdell et al. (1998) and Hsee et al. (1990) noticed that emotional contagion varies by the type of social relationship between the individuals. The experimental

and statistical evidence is consistent with our common sense that people in an enduring and intimate relationship are more susceptible to contagious emotion from each other (Totterdell, Kellett, Teuchmann, and Briner, 1998; Segrin, 2004; Kouros and Cummings, 2010). This contagion effect is particularly true for married couples (Segrin, 2004). Negative emotions such as depression and distress are nearly, if not the same, as contagious as positive emotions (Joiner and Katz, 1999). Therefore, the concordance of depression and distress, considered as long-term consequences of frequent negative emotional experiences, may partly attribute to emotional contagion. It is likely that during daily talks, couples communicate not only words but also their emotions. Theoretically, to test the effect of emotion contagion researchers have to control for many confounding variables and deal with two-way causality problem caused by mutual influence. One challenge is to find some stimulus that applies directly only to one spouse but not the other.

3. Data

3.1. Sample This paper uses data from the China Health and Retirement Longitudinal Study (CHARLS). It is a survey of the elderly in China, based on a sample of household with a member aged 45 or above. CHARLS adopts multi-stage stratified Probability Proportionate to Size (PPS) sampling. The pilot survey of CHARLS was conducted in Gansu and Zhejiang province in 2008 and collected data from 48 communities/village in 16 counties/districts, covering 2,685 individuals living in 1,570 households. The response rate is 85%. The second wave of the pilot survey followed in 2012. In 2011 baseline national wave of CHARLS, they surveyed about 10,000 households and 17,500 individuals in 150 counties/districts and 450 villages/resident committees. In the survey, comprehensive and detailed information about family respondent (aged 45 or older) and his/her spouse are collected (CHARLS, 2014). The household oriented sampling method and large sample size of CHARLS provides an excellent opportunity to explore health concordance within couples. 3.2. Data description The sample in this paper includes respondents who were married with their spouse present. In the sample, I excluded respondents who were married but not living with spouse temporarily for a reason such as work excluded from the sample, mainly due to missing information about their spouse. I also exclude few observations with any missing value in critical variables such as marital status and self-rated health. Data are structured in dyad-level. All the data processing work and regression analysis are done with Stata 12. 3.2.1. Cross-sectional data In models using cross-sectional data, I treat CHARLS national baseline survey (2011) and pilot wave 2 (2012) as two separate cross-sectional datasets. Each dataset adopts the same criteria of data cleaning. The 2011 and 2012 sample contains 784 and 5904 couples with complete information respectively. Table 1a and Table 2a show the sample statistics (See Appendix Table 1a and Table 2a). 3.2.2. Panel data

CHARLS pilot wave 1 (2008) and wave 2 (2012) constitute a short panel. The panel sample includes respondents who were married with the same spouse present in both waves. Demographic and housing characteristics information is based on the survey in 2012. Unfortunately, there are a lot of missing values in the depression score. As a result, the sample size reduced to 478 in the panel after matching, which leads to unclear sample selection bias. Detailed panel sample statistics are shown in Table 3a (See Appendix Table 3a). 3.3. Measure of depression CHARLS ask about depression symptoms in the health status and functioning section. The depression symptom questionnaire containing ten items is a short-form of the Center for Epidemiologic Studies Depression scale (CES-D10), a standard measure of depression symptom containing 20 items (Radloff, 1977). Components include depressed mood, feelings of hopelessness, fearfulness, loneliness, and sadness, poor concentration and sleep disturbance. Two items are worded in a positive direction to control for response bias. Respondents are asked to rate each item on a scale from 1 to 4 on the basis of “How often you have felt and behaved during the last week”, where 1=rarely or none of the time (<1 day), 2= some or a little of the time (1- 2 days), 3=occasionally or a moderate amount of the time (3-4 days), 4= most or all of the time (5-7 days). The total score is calculated by first reversing scores from item DC013 and DC016 (worded in positive direction), and then summing up all the scores. I replace the missing responses with the mean of the other items. Considering the validity of the index, I dropped any observation with more than 5 (out of 10) missing responses in this part, which lead to a reduction in sample size. The final score ranged from 10 to 40, with higher score indicating more severe depression conditions. 3.4. Variables Measures of demographic variables, housing characteristics, physical health status as well as health behavior are included in the model. Negative events and life stressors are counted and used as instrumental variables in certain models. Demographic variables include age, years of schooling, times of marriage, current Hukou status, number of living biological children, number of passed biological children. Few (less than 5) missing values about schooling, current Hukou status are imputed. It is believed that shared resources might lead to depression concordance (Meyler, Stimpson, and Peek, 2007), so I include housing characteristic variables in the model to control for it. Housing characteristics include seven items about housing facilities to measure living condition. These seven dummies are accessibility of electricity, running water, shower or bath facility, natural gas supply, heating, telephone and broadband Internet connection. Consistent with previous research on the mental health concordance of couples, I control for the respondents’ physical health status. Four dimensions of physical health are measured, namely disability, chronic disease, difficulty in daily activities (ADL) and cognitive function. Disability is measured as a dummy variable that equals to 1 whenever there is any disability in the following: physical disability, brain damage/mental retardation, vision problem, hearing problem and speech impediment. ADL is also a dummy variable capturing any difficulty in daily activities in the standard ADL questionnaire. The number of self-reported chronic disease measures chronic disease. For cognitive function, I calculated a cognitive function score based on the questions asked in the Cognitive and Depression section. The score is standardized to range from 0 to 10, with higher score indicating worse cognitive

function. Besides, I use self-rated health as a measurement of subjective feeling about health conditions. The answer is an ordered scale from 1 (very good) to 5 (very poor). For health behavior, dummies about whether smoke or drink alcohol are included. The negative event is defined as the recent death of close relatives. I classify negative events into two types: those common to both parties called shared and those whose primary impact only affects one particular individual named private. Shared negative events include the death of biological/adopted children with the current spouse in the recent one year. Private negative events include the death of own biological parent and additional biological children in the recent one year. Notice that the distinction between shared and private is rather arbitrary. The death of spouse’s parent may also have strong emotional impact on the respondent, especially when they used to live together. Life stressor catches the number of diagnoses of chronic disease in the recent one and a half years. Recent retirement (also within one and a half year) is treated independently as a life stressor. Table 1a, Table 2a and Table 3a in the Appendix contain the summary statistics of the above variables in different samples.

4. Evidence for depression concordance

4.1. Correlation analysis 4.1.1. Spearman rank correlation coefficients In the sample, the depression score is not normally distributed (See Appendix Graph 1a). The skewness and kurtosis tests for normality of the depression scores reject the null hypotheses of normality with p-value<0.000. Therefore, I use Spearman rank correlation coefficients to do the preliminary correlation analysis. Spearman rank correlation is a nonparametric measure of statistical dependence between two variables, therefore is distribution free. It also remains an interesting question of whether changes in depression symptoms are correlated within couples. To investigate into this, I use the short panel data and calculate the correlation between changes in depression scores. 4.1.2. Adjusted correlation Adjusted correlation intends to control for confounding factors in correlation analysis. It has been used in clinical research to study couple similarities for cognitive functions and psychological health (Dufouil and Alpérovitch, 2000). The basic idea comes from residual analysis (Cook and Weisberg, 1982). In calculation, first I do a multiple linear regression to get coefficient estimation for each control variables. In the prediction regression, I control for potential variables that may affect depression symptom, including demographic variables, physical health status, health behavior, negative events and housing characteristics. The model is as follows:

� � � � � �� = � ��ℎ�� + � ��ℎ + � ��ℎ�� + � �� Where denotes+ �� individual� + � and the household. Based on the coefficient estimation, I get the predicted scores for each observation. � �

��̂ � = �̂��ℎ�� + �̂��ℎ� + �̂��ℎ�� + �̂�� + �̂�� 6

The residual, the difference between actual score and the predicted score, is defined as adjusted scores.

��The adjusted �� score�� = �� theoretically� contains− ��̂ potential� omitted variables as well as concordance effect. Therefore the correlation between adjusted scores can be viewed as measurement of concordance after controlling for confounding variables, though it may run the risk of exaggerating the concordance. 4.2. Results 4.2.1. Spearman rank correlation coefficients Table 1 shows the correlation coefficients.

Table 1. Spearman rank correlation coefficients of depression scores between couples Sample Marriage duration Obs Spearman's rho Prob>|t| 2011 All 5904 0.435 0.000 2011 0-29 805 0.435 0.000 2011 30-39 2333 0.409 0.000 2011 40-49 1680 0.421 0.000 2011 50 and above 1086 0.454 0.000 2012 All 784 0.438 0.000 2012 0-29 41 0.410 0.008 2012 30-39 303 0.383 0.000 2012 40-49 235 0.443 0.000 2012 50 and above 205 0.503 0.000 Source: Author’s calculation based on CHARLS 2011,2012

From the correlation coefficients, we could gain a preliminary sense of depression concordance within Chinese mid-aged and elderly couples. All the coefficients are strongly positive and statistically significant. As the marriage duration gets longer, there is no consistent pattern of the change in correlation coefficients. In the sample, the changes in depression score are normally distributed, thus I use Pearson correlation coefficients instead. Table 3 shows the correlation between changes in depression scores during 2008-2012.

Table 3. Spearman rank correlation coefficients of changes of depression scores between couples Sample Marriage duration Obs Correlation Prob>|t| Panel All 478 0.360 0.000 Panel 0-29 26 0.171 0.403 Panel 30-39 205 0.375 0.000 Panel 40-49 139 0.382 0.000 Panel 50 and above 108 0.314 0.000 Source: Author’s calculation based on CHARLS 2008,2012

From the table, we can see that except for the group of couples whose marriage duration is less than 30 years, which only contains limited observations, all other groups shows strong and significant correlation between changes in couples' depression symptoms during the four years between 2008 and 2012. 7

4.2.2. Adjusted correlation Similar to the original depression scores, the distribution of adjusted scores is non- normal. So Spearman rank correlation coefficient is applied again. Table 4 shows the coefficients4.

Table 4. Spearman rank correlation coefficients of adjusted depression scores between couples Sample Marriage duration Obs Spearman's rho Prob>|t| 2011 All 5904 0.355 0.000 2011 0-29 805 0.348 0.000 2011 30-39 2333 0.320 0.000 2011 40-49 1680 0.329 0.000 2011 50 and above 1086 0.378 0.000 2012 All 784 0.282 0.000 2012 0-29 41 0.336 0.031 2012 30-39 303 0.220 0.001 2012 40-49 235 0.294 0.000 2012 50 and above 205 0.311 0.000 Source: Author’s calculation based on CHARLS 2011,2012

From the adjusted correlation coefficients, we can see that after controlling for confounding variables, there is still statistically significant evidence for the depression concordance within couples. The coefficient is smaller compared to that in the preliminary correlation, which is expected because the preliminary correlation is a complex outcome of many confounding variables.

5. Emotional contagion model

5.1. Model specification In this section, I try to set up a model to test the emotional contagion hypothesis. The challenge lies in the bi-direction nature of emotional contagion. While the husband’s depression may affect the wife, the wife’s depression is also expected to influence the husband’s. In statistical terms, there is severe endogeneity problem in any single regression with depression scores as dependent variable and spouse’s depression as independent variable. Therefore I tried instrumental variable methods (IV), structural equation model (SEM) and fixed effect model to address this problem. Models using cross-sectional data and panel data are discussed separately in the following part of this section. 5.1.1. Instrumental Variable – Cross-sectional data IV methods allow researchers to consistently estimate the parameters in a system of simultaneous equations (Angrist and Pischke, 2009). In the following multiple regression model, spouse’s depression is endogenous in that it might be the consequence of the dependent variable itself, in other words, there is two-way causality.

�� = ��′ + ��ℎ�′ + ��ℎ�� + ��ℎ� + ��ℎ�� + ��8�� ′ + �� + �

Where denotes the individual, the spouse and the household. indicates the negative events which have common impact on both parties. The ��′ Durbin-Wu� -Hausman test for endogeneity�′ confirmed that� the spouse’s depression�� is endogenous. A qualified IV should be a variable that is correlated with the causal variables of interest that are subject to endogeneity problem, but uncorrelated with any other determinants of the dependent variable (Angrist and Pischke, 2009). Here I tried three IVs for the endogenous variable spouse’s depression , namely recent private negative events, recent retirement and subjective feeling about health. A critical assumption is that these three factors have �� direct impact�′ on depression symptoms of the spouse, but not at least directly on the respondent. A private negative event includes the recent death of biological parent and additional biological child (not with the respondent) of the spouse. It is a candidate IV in that such events are expected to exert much stronger impact on the spouse compared to the respondent. Retirement is a milestone life event, therefore will influence depression status. However, it is possible that the changes in lifestyle due to one’s retirement have impact on the spouse’s depression. A special IV used here is the subjective feeling about health. It is defined as the residue from the following regression:

� �� − �� ℎ��ℎ � � � � = � ��ℎ�� + � ��ℎ + � ��ℎ�� + � �� The idea is + that � �� the residue� + �measures the subjective feeling about health after controlling for actual physical health conditions and other variables, though the physical health indices have not covered every aspect of physical health. In this case, even if one’s health condition will influence his/her spouse’s depression symptoms via mechanisms like caregiving, we could argue that the subjective feeling of health is only directly related to the subject’s depression conditions. If this argument holds, then the subjective feeling about health could be an IV for spouse’s depression in the regression. With the above concerns in mind, I did test for weak IV as well as over- identification. In the first stage regressions with all three IVs included, the F-stat is F (3, 5904) = 116.10 and F (3, 5904) = 93.74 for two separate regression for both sexes (wife to husband and husband to wife). Both of the p-values < 0.000. The Shea’s adjusted partial is 0.052 and 0.041, and the weak IV test rejected the null hypothesis of weak instruments. Over-identification test cannot reject the null hypothesis that � the excluded instruments are valid instruments with p > 0.89. Therefore, the candidate IVs are acceptable. However, when the IV is used individually, the recent private negative events and recent retirement fail to pass the above tests. It turns out that the only valid IV is subjective feeling about health. Its corresponding F-stat is F (1, 5877)=347.9 and F (1, 5877)=275.9, while Shea’s adjusted partial =0.05 and =0.04 for separate regression for both sexes as above. 5.1.2. Structural Equation Model � � Small-scale SEM has gained popularity in studies where simultaneous effects occur between variables. It is especially useful in Actor-Partner relationship analysis (Kenny and Ledermann, 2010). The following diagram illustrates the SEM model for emotional contagion. 9

Graph 1. SEM model for emotional contagion

The equivalent equation representation is:

฀ �� = ��′��′ + ��′��ℎ�′ + ��ℎ�� + ��ℎ� + ��ℎ�� + ��′ + �� + � �� + �� ′ � ′ � � �′ �′ �′ �′ �� = � �� + � ��ℎ + � ��ℎ�� + � ��ℎ �′ �′ �′ ��′ �′ �′ �′ � + � ��ℎ�� + � �� + � �� + � �� Notice that + classical ��′ SEM requires multivariate normal distributions, which is a rather strong requirement of the sample distribution. According to the sample in this paper, the depression scores are not normally distributed, with null hypothesis of normality rejected at p-value<0.000. Even though I use an asymptotic distribution- free method to alleviate the non-normality problem, the non-normality problem will lead to bias in the estimation. Therefore, I incorporate fixed effect into SEM model to deal with this issue. 5.1.3. Fixed effect – Panel data The gaining in using panel data is in that I am thus able to control for fixed effects. In the regression using cross-sectional data, I cannot control potential confounding variables such as personal characteristics, shared environment and even genetic factors related to depression. In other words, the following model may be closer to the real mechanism:

� �′ �′ �′ �′ � � � � �� = � �� + � ��ℎ + � ��ℎ�� + � ��ℎ � � � ��′ � � � � + � ��ℎ�� + � �� + � �� + � �� Where denotes+ � + person � + � ‘s unobserved characteristics, and denotes that of household . An underlying assumption is that both unobserved characteristics are � � time-invariant.� Within a relatively� short period, this assumption� holds for stable characteristics� such as personality and certainly for genetic factors. However, the housing features and shared environment, in general, could go through substantial

changes. Here for model simplicity, I assume that both and are constant in the panel. Taking time into consideration, I have the following model: ��

�� = ��′��′� + ��′��ℎ �′� + ��ℎ�� ′� � �� + � ��ℎ + � ��ℎ�� + � �� + � �� Since the panel+ �� only�� contains� + � � two+ �� waves,+ �� taking difference and demeaning is theoretically the same. Here I differencing the equations and get:

∆ �� = ��′∆��′ + ��′∆��ℎ�′ + ��∆��ℎ� + ��∆��′ All the effects+ �of� variables∆�� that+∆� could� be considered as constant between the survey waves are eliminated after first order differencing. Nevertheless, this equation is still subject to endogeneity problem in that the changes in depression of couples are inter- related. The IV method using subjective feeling of health is no longer valid here because subjective feeling of health is supposed to catch long-term depression factors and its variation between waves might be too small. Therefore, I use SEM model to estimate the following equations:

′ ′ ′ ′ � � � � � � � ∆�� = � ∆�� + �′ ∆��ℎ + � ∆��ℎ � �� ′ +� ∆�� + � ∆�� +∆�′ ′ ∆�� = ��∆�� + ��∆��ℎ� + �� ∆��ℎ� +��′∆��′ + ��′∆��′+∆��′ 5.2. Results For cross-sectional data analysis, I only display the result of 2011 national baseline data. Results using pilot survey data are similar. The data is structured in dyad-level, so I analysis husbands and wives sample separately. Table 5 presents the key results of IV regression using cross-sectional data (Full table see Appendix Table 4a). Table 5 shows the first-stage regression summary statistics.

Table 5. Results for IV estimation (1) (2) (3) (4) (5) (6) OLS OLS IV IV IV IV VARIABLES Depression Depression Depression Depression Depression Depression (Husband) (Wife) (Husband) (Wife) (Husband) (Wife)

Passed children 0.394** 0.302 0.322* 0.147 0.322* 0.150 (0.174) (0.190) (0.178) (0.200) (0.178) (0.199) Common events 0.424** -0.176 0.427** -0.261 0.427** -0.26 (0.209) (0.236) (0.211) (0.241) (0.211) (0.241) Disability 0.917*** 1.506*** 0.891*** 1.405*** 0.891*** 1.407*** (0.175) (0.214) (0.176) (0.220) (0.176) (0.220) ADL 2.622*** 2.734*** 2.537*** 2.626*** 2.537*** 2.628*** (0.180) (0.181) (0.185) (0.187) (0.185) (0.187) Number of chronic 0.760*** 0.899*** 0.741*** 0.878*** 0.741*** 0.878*** disease (0.049) (0.054) (0.050) (0.055) (0.050) (0.055) Cognitive function 0.357*** 0.382*** 0.343*** 0.367*** 0.343*** 0.367*** (0.044) (0.046) (0.044) (0.047) (0.044) (0.047) Disability (Spouse) -0.103 -0.160 -0.278 -0.376* -0.278 -0.371* (0.191) (0.198) (0.206) (0.212) (0.206) (0.211) ADL (Spouse) -0.455*** -0.357* -0.762*** -0.973*** -0.762*** -0.961*** (0.163) (0.207) (0.210) (0.284) (0.210) (0.282) Number of chronic -0.199*** -0.155*** -0.298*** -0.323*** -0.298*** -0.320*** disease (Spouse) (0.049) (0.056) (0.064) (0.078) (0.064) (0.077) Cognitive function -0.054 -0.032 -0.097** -0.126** -0.097** -0.124** (Spouse) (0.039) (0.048) (0.044) (0.057) (0.044) (0.057) Smoke 0.252* 0.011 0.229 0.111 0.229 0.109 (0.144) (0.274) (0.145) (0.280) (0.145) (0.280) Drink alcohol -0.233* 0.511** -0.210 0.481** -0.210 0.481** (0.129) (0.224) (0.130) (0.228) (0.130) (0.228) (0.711) (0.793) (1.002) (1.296) (1.002) (1.288)

Observations 5,904 5,904 5,904 5,904 5,904 5,904 R-squared 0.319 0.331 0.308 0.304 0.308 0.305

IV Subjective feeling 1 1 1 1 about health Recent private events 1 1 Recent retirement 1 1

First-stage regression Partial 0.056 0.045 0.056 0.046 F-stat 347.94 275.90 116.10 93.74 (p-value)� (0.000) (0.000) (0.000) (0.000) Shea’s adjust partial 0.052 0.041 0.052 0.041 Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 � The depression score is constructed in the way that higher score indicating worse conditions so that variables with positive coefficient can be viewed as protective factors while the negative ones the risk factors. From the result, the coefficients of spouse’s depression remain significant after controlling for the physical health of both, which is a strong sign for emotional contagion effect because the caregiving effect and shared event, as well as environment, have been controlled as much as possible. Wives are more sensitive to the negative emotion of their husband compared with the other way around. This finding is consistent with our daily experience that women are more sensitive to other's feelings, especially their significant ones. The effects of other factors are also of interest. Couples with non-agriculture hukou are less depressed, which is consistent with the report that depression has been more prevalent and severe in Chinese rural areas. As expected, good health condition is a significant protective factor for both genders. However, it is hard to explain why all the coefficients of the spouse’s physical health conditions turn out to be negative and 12

sometimes even statistically significant, indicating a negative correlation between one's depression and spouse's physical health. It is quite counterintuitive and inconsistent with the caregiving effect hypothesis. This phenomenon calls for further research. Interestingly enough, some factors show gender difference in their effect to depression. As men get older, they tend to be less depressed, while such phenomenon is insignificant for women. Childhood condition represented by whether living in city/town instead of village before age 16 have significant and considerable opposite effect on male and female, which remains an interesting question to explore. Another interesting gender difference lies in the effect of having access to broadband Internet, in that it is a strong protective factor for women but not for men. Perhaps it is related to the fact that women tend to be more social than men. Drinking alcohol is another factor that exhibits gender difference, though it is hard to tell if it is because people who are depressed would want a drink. Table 6 shows the result of fixed effect model with SEM estimation.

Table 6. Results for fixed effect model with SEM estimation (1) (2) VARIABLES Δ Depression Δ Depression (Wife) (Husband)

Δ Depression (Spouse) -0.060 0.341* (0.837) (0.066) Common events 0.488 0.416 (0.480) (0.288) Private events -1.047 -0.755 (0.310) (0.190) Recent retirement 0.629 -1.247 (0.871) (0.130) Recent diagnose -0.278 1.165*** (0.413) (0.000) Δ Disability 0.644 0.704 (0.476) (0.277) Δ ADL 2.727*** 0.929* (0.000) (0.066) Δ Number of chronic 0.589*** 0.352* disease (0.007) (0.057) Δ Cognitive function 0.329** 0.218** (0.013) (0.050) Δ Disability (Spouse) 1.700** 1.393** (0.018) (0.032) Δ ADL (Spouse) 0.084 0.695 (0.912) (0.346) Δ Number of chronic -0.063 -0.150 disease (Spouse) (0.855) (0.415) Δ Cognitive function -0.101 -0.166 (Spouse) (0.496) (0.181) Constant 0.058 -0.930*** (0.871) (0.001) Observation 5904 5904

p-value in parentheses; *** p<0.01, ** p<0.05, * p<0.1

tThe fixed effect model estimation results are consistent with the findings from IV regression, though they are not the same. The key variable, the changes in spouse’s depression, is statistically significant for males, which tells a different story from the IV estimation where wives are more sensitive to the emotion status of their husband. When either one’s physical condition gets worse, especially regarding disability and ADL, couples become more depressed. The coefficients exhibit less variance across genders here than in the IV estimation, with the exception that males seem to be more upset with their recent diagnosis of chronic disease while females are influenced more by worsen condition measured by ADL. The fixed effect model further supports the emotional contagion effect. By taking difference, I eliminate the fixed effects and rule out the effect of assortative mating. As I further controlled for shared events, the significant coefficient of spouse’s depression can be interpreted as emotional contagion, though there might still be confounding effects from unobserved shared environment.

6. Conclusions and discussions

6.1. Conclusions According to CHARLS samples of Chinese couples aged 45 or above, this research shows significant concordance regarding depression symptoms. After controlling for demographic variables, housing characters, physical health conditions and health behaviors, the adjusted correlation is still significant, confirming the depression concordance within Chinese couples. Using IV methods and SEM, I estimated the coefficient of mutual influence of depression. The IV estimation supports the emotional contagion hypothesis. There is stronger evidence for the depression contagion from husband to wife, compared to the counter direction. Fixed effect model estimated by SEM method gives more evidence for emotional contagion effect. 6.2. Discussions The research in this paper is just a preliminary one with large potential for improvement. Firstly, couples are nested in the household, and such data structure is best dealt with in multi-level regression models. There has been much research applying multi-level regression models to dyadic data (Kenny, Korchmaros, and Bolger, 2003; Bauer Preacher and Gil, 2006; Preacher, Zyphur and Zhang, 2010), which could shed some light on the study of depression concordance. Besides, evidence shows that the contagion effect is associated with marriage duration (Eagles et al., 1987; Druley et al., 2003; Siegel et al., 2004). It may be that the longer the couples are in marriage, the more sensitive they are to each other’s emotional fluctuation. A practical way to test it is to add an interaction term between marriage duration and spouse’s depression score in the models. However, statistical inference of interaction term in SEM requires further study.

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Appendix

Table 1a. Sample statistics of Chinese spouse from CHARLS pilot wave 2 2012

Obs = 784 Husband Wife Variable Mean S.D. Mean S.D. Min Max Correlation Demographics

Age 62.43 8.85 58.85 8.79 38 93 0.92 Urban Hukou 0.25 0.43 0.19 0.39 0 1 0.77 Live in City/Town before 16 0.91 0.28 0.92 0.28 0 1 0.51 Years of schooling 5.32 4.01 2.79 3.73 0 16 0.39 Times of marriage 1.04 0.20 1.03 0.18 1 2 0.57 Number of living biological children 2.31 1.37 2.34 1.38 0 9 0.95 Number of passed biological children 0.08 0.34 0.08 0.33 0 4 0.98 Events

Private events 0.03 0.17 0.03 0.18 0 1 -0.03 Common events 0.07 0.25 0.07 0.25 0 1 1.00 Recent retirement 0.01 0.09 0.01 0.09 0 1 -0.01 Recent diagnose 0.23 0.61 0.22 0.60 0 6 0.07 Health behavior Smoke 1.24 0.43 1.98 0.13 1 2 -0.07 Drink alcohol 0.52 0.50 0.14 0.34 0 1 0.17 Health

Self-rated health 3.35 1.09 3.56 1.10 1 5 0.21 Depression symptoms 17.18 6.01 19.03 7.00 10 40 0.43 Disability (dummy) 0.16 0.37 0.13 0.33 0 1 0.19 ADL (dummy) 0.19 0.39 0.26 0.44 0 1 0.25 Number of chronic disease 1.43 1.39 1.50 1.47 0 8 0.20 Cognitive function index 5.73 1.77 6.28 1.96 0.4 10 0.40 Housing characteristics

Electricity 0.96 0.19 0.96 0.19 0 1

Running water 0.84 0.37 0.84 0.37 0 1

Shower facility 0.52 0.50 0.52 0.50 0 1

Natural gas 0.09 0.28 0.09 0.28 0 1

Heating 0.07 0.26 0.07 0.26 0 1

Telephone 0.56 0.50 0.56 0.50 0 1

Broadband internet 0.25 0.43 0.25 0.43 0 1 Source: Author’s calculation from CHARLS pilot wave 2 2012

Table 2a. Sample statistics of Chinese spouse from CHARLS national base line 2011

Obs = 5904 Husband Wife

Variable Mean S.D. Mean S.D. Min Max Correlation Demographics

Age 59.26 9.28 56.99 8.98 22 93 0.92 Urban Hukou 0.24 0.43 0.19 0.39 0 1 0.80 Live in City/Town before 16 0.90 0.31 0.91 0.29 0 1 0.61 Years of schooling 6.72 3.84 4.25 4.15 0 19 0.46 Times of marriage 1.04 0.22 1.04 0.20 1 6 0.51 Number of living biological children 2.58 1.34 2.60 1.34 0 14 0.96 Number of passed biological children 0.09 0.37 0.10 0.38 0 5 0.95 Events

Private events 0.03 0.19 0.04 0.21 0 2 0.08 Common events 0.08 0.30 0.08 0.30 0 4 1.00 Recent retirement 0.01 0.10 0.01 0.08 0 1 0.03 Recent diagnose 0.23 0.57 0.23 0.54 0 6 0.06 Health behavior Smoke 0.74 0.44 0.07 0.26 0 1 0.01 Drink alcohol 0.57 0.50 0.11 0.32 0 1 0.09 Health

Self-rated health 3.40 1.01 3.58 0.99 1 5 0.19 Depression symptoms 17.14 5.77 19.14 6.55 10 40 0.44 Disability (dummy) 0.18 0.38 0.14 0.35 0 1 0.24 ADL (dummy) 0.18 0.38 0.25 0.43 0 1 0.26 Number of chronic disease 1.31 1.35 1.41 1.42 0 10 0.25 Cognitive function index 5.58 1.67 5.86 1.84 0 10 0.41 Housing characteristics

Electricity 0.82 0.38 0.82 0.38 0 1

Running water 0.62 0.49 0.62 0.49 0 1

Shower facility 0.39 0.49 0.39 0.49 0 1

Natural gas 0.12 0.32 0.12 0.32 0 1

Heating 0.09 0.29 0.09 0.29 0 1

Telephone 0.51 0.50 0.51 0.50 0 1

Broadband internet 0.17 0.38 0.17 0.38 0 1

Source: Author’s calculation from CHARLS national baseline 2011

Table 3a. Sample statistics of short panel from CHARLS pilot wave 1(2008) and 2 (2012)

Obs = 478 Husband Wife Variable Mean S.D. Mean S.D. Min Max Correlation Demographics

Age 61.60 8.35 57.95 8.34 38 86 0.92 Urban Hukou 0.26 0.44 0.22 0.41 0 1 0.79 Live in City/Town before 16 0.91 0.29 0.91 0.28 0 1 0.50 Years of schooling 5.38 4.01 3.09 3.82 0 16 0.36 Times of marriage 1.05 0.21 1.04 0.19 1 2 0.62 Number of living biological children 2.21 1.26 2.22 1.25 0 8 0.95 Number of passed biological children 0.09 0.36 0.09 0.35 0 4 0.98 Events

Private events 0.07 0.28 0.10 0.32 0 2 0.01 Common events 0.17 0.43 0.17 0.43 0 3 1.00 Recent retirement 0.01 0.09 0.01 0.09 0 1 -0.01 Recent diagnose 0.55 0.94 0.45 0.77 0 5 0.06 Health behavior Smoke 1.24 0.43 1.99 0.12 1 2 0.03 Drink alcohol 0.53 0.50 0.15 0.35 0 1 0.19 Health

Self-rated health 2012 3.31 1.06 3.54 1.10 1 5 0.23 Self-rated health 2008 3.54 1.07 3.73 1.03 1 5 0.28 Depression symptoms 2012 16.80 5.82 18.53 6.86 10 40 0.44 Depression symptoms 2008 16.72 5.37 18.20 6.08 10 40 0.50 Disability (dummy) 2012 0.14 0.35 0.10 0.30 0 1 0.21 Disability (dummy) 2008 0.08 0.27 0.06 0.24 0 1 0.02 ADL (dummy) 2012 0.16 0.37 0.20 0.40 0 1 0.19 ADL (dummy) 2008 0.10 0.31 0.19 0.39 0 1 0.34 Number of chronic disease 2012 1.47 1.37 1.44 1.46 0 7 0.21 Number of chronic disease 2008 1.04 1.07 1.07 1.27 0 8 0.14 Cognitive function index 2012 5.63 1.69 6.06 1.90 0.4 10 0.37 Cognitive function index 2008 5.45 1.65 5.83 1.93 0 10 0.32 Housing characteristics

Electricity 0.97 0.17 0.97 0.17 0 1

Running water 0.87 0.34 0.87 0.34 0 1

Shower facility 0.54 0.50 0.54 0.50 0 1

Natural gas 0.10 0.30 0.10 0.30 0 1

Heating 0.07 0.26 0.07 0.26 0 1

Telephone 0.56 0.50 0.56 0.50 0 1

Broadband internet 0.27 0.45 0.27 0.45 0 1 Source: Author’s calculation from CHARLS pilot wave 1 2008 and wave 2 2012

Graph 1. Histogram of depression symptom in sample CHARLS national base line 2011

Table 4a. Results for IV regression

(1) (2) (3) (4) (5) (6) OLS OLS IV IV IV IV VARIABLES Depression Depression Depression Depression Depression Depression (Husband) (Wife) (Husband) (Wife) (Husband) (Wife)

Depression (Spouse) 0.320*** 0.408*** 0.425*** 0.616*** 0.425*** 0.612*** (0.011) (0.014) (0.046) (0.066) (0.046) (0.065) Age -0.025*** -0.022** -0.021** -0.010 -0.021** -0.010 (0.009) (0.010) (0.009) (0.011) (0.009) (0.011) Hukou -0.544*** -0.561** -0.460** -0.479* -0.460** -0.480* (Non-agriculture) (0.195) (0.249) (0.199) (0.255) (0.199) (0.255) Live in city/town -0.529** 0.479 -0.541** 0.549* -0.541** 0.547* before 16 (0.247) (0.300) (0.248) (0.306) (0.248) (0.306) Years of schooling -0.066*** 0.011 -0.062*** 0.019 -0.062*** 0.019 (0.020) (0.022) (0.020) (0.022) (0.020) (0.022) Times of marriage 0.003 0.376 -0.042 0.301 -0.042 0.302 (0.281) (0.345) (0.284) (0.352) (0.284) (0.351) Living children 0.042 0.053 0.033 0.019 0.033 0.019 (0.055) (0.063) (0.056) (0.065) (0.056) (0.065) Passed children 0.394** 0.302 0.322* 0.147 0.322* 0.150 (0.174) (0.190) (0.178) (0.200) (0.178) (0.199) Common events 0.424** -0.176 0.427** -0.261 0.427** -0.26 (0.209) (0.236) (0.211) (0.241) (0.211) (0.241) Disability 0.917*** 1.506*** 0.891*** 1.405*** 0.891*** 1.407*** (0.175) (0.214) (0.176) (0.220) (0.176) (0.220) ADL 2.622*** 2.734*** 2.537*** 2.626*** 2.537*** 2.628*** (0.180) (0.181) (0.185) (0.187) (0.185) (0.187) Number of chronic 0.760*** 0.899*** 0.741*** 0.878*** 0.741*** 0.878*** disease (0.049) (0.054) (0.050) (0.055) (0.050) (0.055) Cognitive function 0.357*** 0.382*** 0.343*** 0.367*** 0.343*** 0.367*** (0.044) (0.046) (0.044) (0.047) (0.044) (0.047) Disability (Spouse) -0.103 -0.160 -0.278 -0.376* -0.278 -0.371* (0.191) (0.198) (0.206) (0.212) (0.206) (0.211) ADL (Spouse) -0.455*** -0.357* -0.762*** -0.973*** -0.762*** -0.961*** (0.163) (0.207) (0.210) (0.284) (0.210) (0.282) Number of chronic -0.199*** -0.155*** -0.298*** -0.323*** -0.298*** -0.320*** disease (Spouse) (0.049) (0.056) (0.064) (0.078) (0.064) (0.077) Cognitive function -0.054 -0.032 -0.097** -0.126** -0.097** -0.124** (Spouse) (0.039) (0.048) (0.044) (0.057) (0.044) (0.057) Smoke 0.252* 0.011 0.229 0.111 0.229 0.109 (0.144) (0.274) (0.145) (0.280) (0.145) (0.280) Drink alcohol -0.233* 0.511** -0.210 0.481** -0.210 0.481** (0.129) (0.224) (0.130) (0.228) (0.130) (0.228) Electricity 0.039 0.157 0.019 0.136 0.019 0.137 (0.180) (0.203) (0.181) (0.207) (0.181) (0.206) Water -0.315** 0.220 -0.326** 0.280 -0.326** 0.279 (0.149) (0.168) (0.150) (0.172) (0.150) (0.172) Bath -0.630*** -0.614*** -0.525*** -0.411** -0.525*** -0.415**

(0.149) (0.169) (0.157) (0.183) (0.157) (0.182) Gas -0.038 0.096 -0.044 0.112 -0.044 0.111 (0.231) (0.263) (0.233) (0.267) (0.233) (0.267) Heating -0.345 -0.196 -0.299 -0.100 -0.299 -0.102 (0.244) (0.278) (0.247) (0.284) (0.247) (0.284) Telephone -0.175 -0.172 -0.148 -0.113 -0.148 -0.114 (0.131) (0.148) (0.132) (0.151) (0.132) (0.151) Broadband -0.201 -0.550** -0.126 -0.442** -0.126 -0.444** (0.195) (0.219) (0.199) (0.226) (0.199) (0.225) Constant 11.00*** 8.660*** 9.351*** 5.376*** 9.350*** 5.438*** (0.711) (0.793) (1.002) (1.296) (1.002) (1.288)

Observations 5,904 5,904 5,904 5,904 5,904 5,904 R-squared 0.319 0.331 0.308 0.304 0.308 0.305

IV Subjective feeling 1 1 1 1 about health Recent private events 1 1 Recent retirement 1 1

First-stage regression Partial 0.056 0.045 0.056 0.046 F-stat 2 347.94 275.90 116.10 93.74 (p-value)� (0.000) (0.000) (0.000) (0.000) 0.052 0.041 0.052 0.041 Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 Shea’s adjust partial