Women’s Education and Household Decision Making from a Multi-Generational Perspective

Cheng Cheng, Princeton University

ABSTRACT

Past research on how higher education of married women may increase their decision making power at home focused primarily on nuclear families. This paper extends prior research and examines how the relationship between a married woman’s education and her household decision making power vary by household structure. Inverse probability of treatment weighting results using longitudinal data from China Family Panel Studies suggest that in nuclear households, higher education of women is associated with higher probability of them being the decision maker. However, in multi-generational households where women live with their parents-in-law, higher level of education of women does not increase their decision making power. These results shed light on the implications for aging societies for marital power inequality.

This is a work in progress. INTRODUCTION

There has been extensive work on how higher education of married women may increase their decision making power at home, dating at least back to the 1960s (Blood and Wolfe, 1960;

Safilios-Rothschild 1970; McDonald 1980). These studies have important implications for how improving educational attainment of women may empower them at home. However, most of such work focused primarily on nuclear families and was thus based on the premise that only the married couple was involved in the competition for household decision making power. Would higher education also improve married women’s decision making power at home in multi- generational households? How might the presence of the older generation in the household moderate the effect of married women’s educational attainment on their decision making power?

This paper extends previous research and examines how the relationship between a married woman’s education and her power to make household decisions vary by household structure.

Multigenerational relations and households are becoming increasingly important for our societies, as longer life expectancies and population aging have created more opportunities for intergenerational interactions (Bengtson 2001). In many Asian societies as well as many other developing countries, intergenerational co-residence among married couples with their elderly parents was historically prevalent and is still increasing (Esteve and Liu 2014; Ruggles and

Heggeness 2008). Understanding how higher education of women may be related to their household decision making power in multi-generational households has important implications for what aging societies mean for marital power inequality. In addition, the presence of the older generation in the household may change family dynamics and how decision making power gets distributed. For example, when the husband’s parents join the nuclear household, the older generation brings in not only more potential competitors for decision making power, but also a distinctive form of resources for the husband, given their connection to the husband by blood and their reliance on the husband for future help. Both of these processes may mitigate how much the wife’s education could translate into her bargaining leverages against her husband and in-laws for decision making power.

China offers a particularly interesting context to examine these questions. First, multi- generational co-residence remains prevalent in China. According to data from the nationally representative 2011 China Health and Retirement Longitudinal Study (CHARLS), about 41% of

Chinese elderly aged 60 and over live with an adult child and 34% live in the same immediate neighborhood as an adult child (Lei et al. 2015). Children remain the primary source of old age financial and instrumental support for elderly parents, due to the continuity of strong filial norms and lack of formal old age support (Chu et al. 2011). With China’s unparalleled aging trend

(Chen and Liu 2009), multi-generational households will become more important in the future.

Second, gender equality in education in China has improved significantly, yet patriarchal norms are still pervasive in other realms. The gender gap in mean years of schooling has been closing over time in both rural and urban China. In urban China, men and women of the post-1980 cohort have even reached parity in average years of schooling (Wu and Zhang 2010). However, household division of labor remains unequal, even among dual earner couples, and both wives and husbands perceive such division as fair (Zuo and Bian 2001). Although women’s employment rate in China remains high, 70% among women aged 16-59 in 2010, severe occupational segregation and large gender wage gap persist (Wu and Wu 2008; Li and Xie

2013).

This paper focuses on the comparison between nuclear families and multi-generational households where women live with their parents-in-law. This is because multi-generational co- residence in China remains predominantly patrilocal. The vast majority of elderly parents who live with their adult children live with their sons and daughters-in-law (Chu et al. 2011). As will be discussed in the data section, the number of cases where women live with their own parents is too small to draw meaning conclusions.

This paper employs inverse probability of treatment weighting (IPTW) using propensity scores to examine the interaction between women’s education and household structure on their decision making power. IPTW methods allow me to address two important theoretical complexities in this study. First, household structure is endogenous to decision making. Pre- existing characteristics that may select women of less power into certain living arrangements.

IPTW allows me to adjust for a large number of baseline covariates that conventional regression methods would not. Second, household structure may work as one of the mechanisms through which education influences power. More educated women are more likely to live independently and thus enjoy more decision making power. IPTW methods allow me to incorporate this process into the estimation of propensity score models that other propensity score methods would not.

This paper is structured as follows. In the next section, I will introduce the existing theory on why women’s education may improve their decision making power and extend this theory and discuss why household structure may moderate the relationship between women’s education and their decision making power. Then I will discuss the data, the analytic sample, and measurements of variables. In the methods section, I will explain the implementation of the

IPTW methods and discuss the advantages of IPTW methods over conventional regression and other matching methods. Finally, I will present the preliminary results and their implications. THEORETICAL FRAMEWORK

Women’s Education and Household Decision Making in Nuclear Families

The question of how women’s education is related to their decision making power at home stems from a long history of resource theory in family sociology, first developed by Blood and Wolfe 1960 in their Detroit study of Husbands and Wives’ decision making. The central premise of the resource theory is that, within the marital relationship, each spouse’s decision making power varies directly with the amount and value of the resources that the spouse provides to the marriage. A resource may be defined as anything that one partner may make available to the other, helping the latter satisfy his/her needs. The balance of power will be on the side of that partner who contributes the greater resources to the marriage (Blood and Wolfe,

1960: 12). Resource theory is thus an application of a rather simplified form of exchange theory to the marital arena (Safilios-Rothschild 1970). Resources are viewed as bargaining leverages in an exchange relationship. Each spouse provides the other with access to his/her resources in exchange for compliance. The spouse with few resources is in a position of dependence, which engenders feelings of indebtedness and a motivation to avoid alienating the provider of the valued resources. Thus resources and conjugal power are hypothesized to be positively related.

This study focuses on education as a form of resources and bargaining leverages exchanged in marriage. Unlike other forms of socioeconomic resources such as employment and income, education is less prone to endogeneity issues. Women with less decision making power at home may be more likely to give up their own careers after marriage. In contrast, educational attainment is relatively stable in adulthood. In addition, this study focuses on the absolute level of education attained by women. It is worth noting that both the absolute level of education attained by women and the relative level of education of women compared to their husbands’ may influence decision making power of women. Wives having higher education than their husbands means that they have more leverage than their husbands. But women with higher absolute level of education, even though they may still have less education their husbands, still have a higher chance of being the household decision maker than women with lower absolute level of education. It is not fair to compare women with college degree but less education than their husbands with women with high school degree but more education than their husbands. The main purpose of this paper is not to argue the implications of women’s choice of spouse for their power at home, but the implications of increasing women’s education for their power at home. It is not about how marrying someone with lower education may increase one’s power at home, but how improving women’s educational attainment may empower them at home. Hence, wife’s absolute level of education is the main independent variable and husband’s education will be accounted for in the analyses. In summary, this paper hypothesizes that in nuclear households, higher absolute level of education of married women is associated with higher probability of being the decision maker at home.

Women’s Education and Household Decision Making in Multi-Generational Families

How might the relationship between women’s education and their decision making power differ in multi-generational households? How might the presence of the older generation, in particular, women’s in-laws, changes family dynamics and how decision making power is distributed?

For starters, as the presence of in-law(s) bring in more adult(s) to the household, household decision making power gets competed among more people. In nuclear families, spouses are more directly interdependent (Whyte 1978). The level of education attained by a married woman directly reflects the level of leverage she has against her husband in decision making. However, when there are more adult(s) involved, how much the wife’s education could translate into her bargaining leverages against her husband and in-laws for decision making power may be undermined, because the bargaining value of her educational attainment is evaluated by not only her husband, but also the older generation.

Furthermore, the wife’s in-laws represent a distinctive form of resource for the husband.

From the in-laws’ point of view, it is only the husband who is connected to them by blood and hence who they ultimately rely on for future help if needed. From the husband’s point of view, there is a need to maintain the intergenerational order in the household to secure their parents’ old age benefits (Zuo 2009). Hence, how much the wife’s education could translate into her bargaining leverages against her husband and in-laws for decision making power may be very limited.

Finally, in a patriarchal and patrilocal society like China, power in multi-generational households are guided by not only gender, but also seniority. This is known as “generation patriarchy”. Both husbands and wives are subject to the power of the parents, which puts wives in the bottom of the hierarchy. But even in cases when older generation’s power diminish with aging parents’ capabilities to run the family, generation patriarchy also increased junior men’s responsibility to carry on the family mission of preserving the male lineage, which increased power use by junior men against their wives. This is known as “Substitute generation patriarchy”. Men fulfil their role as a filial son, more than a protective husband (Zuo 2009). The wife is supposed to bear most of the responsibilities of childcare and housework and serve the needs of all other family members (Chen 2004).

In summary, this paper hypothesizes that in nuclear households, higher absolute level of education of married women is associated with higher probability of being the decision maker at home. However, in multi-generational households where married women live with their parents- in-law, higher absolute level of education of women does not increase their decision making power.

DATA

This paper uses 2010 and 2014 data from the China Family Panel Studies (CFPS). The

CFPS is a nationally representative longitudinal survey, conducted biennially since 2010. In

2010, the CFPS interviewed 14,690 households and their 42,590 family members from 25 provinces or their administrative equivalents in China1, using multi-stage probability proportion to size sampling with implicit stratification (Xie and Hu 2014). The response rate was 81.3% at the household level and 84.1% at the individual level. In 2014, the re-interview rate was 83.1% at the household level and 76.2% at the individual level. The CFPS consists of five questionnaires, 1) the community questionnaire answered by a community administrator, 2) the family roster questionnaire completed by the family member most knowledgeable about family relations, 3) the family questionnaire answered by one or multiple family members most knowledgeable about family economics, 4) the adult questionnaire answered by each adult family members in the household, and 5) the child questionnaire completed by each child family member in the households and their caretakers.

The major advantage of CFPS for the purpose of this paper is that it collects basic demographic and socioeconomic information on all household members and on all spouses, children, siblings, and parents of the household members. This makes it possible to examine family dynamics from a multi-generational perspective.

1 These 25 provinces represent 94.5% of the total population in mainland China. Hong Kong, Macao, Xinjiang, Tibet, Qinghai, Inner Mongolia, Ningxia, and Hainan are excluded. Shanghai, Liaoning, Henan, Gansu, and Guangdong are oversampled. Analytic Sample

The unit of analysis is woman, as the research question is about how woman’s education is related to their decision making power at home. The analytic sample is constructed in the following steps. First, I restrict the sample to women who were married in 2010 and 2014 with the same spouse. This is because information on whether respondent’s and spouse’s parents are alive was only complete in 2010. The vast majority of women who were married in 2010 remained married till 2014 and remained married to their original spouse in 2010. Second, the sample is restricted to those with at least one living in-law in 2010. This is to ensure that each woman has a non-zero probability of living with her in-law at least in 2010. Because data on non-resident parents and in-laws were in majority not updated in 2014, I can only assume that deaths of in-laws were rare events between 2010 and 2014 and do not confound the relationship between education, household structure, and decision making power beyond the observed characteristics. Finally, the sample is restricted to those who live with their husbands in 2014, so that each woman has a non-zero probability of being in the nuclear setting. The size of the final analytic sample is 2,866.

Measurements

The outcome of interest is household decision making. It is measured in 2014 as whether the woman is the final decision maker of each of the following household decisions, 1) allocation of household expenditure, 2) savings, investment, and insurance, 3) purchasing house, 4) educating children, and 5) purchasing high value consumer products (e.g. refrigerator, air- conditioner, furniture set). Separate models are estimated for each decision. Questions regarding household decision making in the CFPS are answered by the family module respondent. Such respondent is selected based on the procedure in which the interviewer explains that the following module is with regards to the family structure and basic information of family members, asks who the most appropriate person to answer this module is, and selects such a household member aged 18 and over. The module then asks for each of the household decisions, who mainly makes the final decision. The interviewer is instructed to select the final decision maker.

The independent variable is the highest level of education attained by a woman. It includes the following five categories, 1) less then elementary school, 2) elementary school, 3) middle school, 4) high school, and 5) college education and above.

The moderator is whether the married couple lives with the husband’s parent(s) in 2014.

The very few (less than 100) cases where the married couple lives with the wife’s parent(s) were excluded from the analyses. Hence, the analyses compare married women living in nuclear households and those living with their in-laws.

The choice of covariates and their measurements will be discussed in the next section in the context of constructing propensity score models. All covariates are measured at baseline in

2010.

METHODS

To examine how the relationship between married women’s education and their decision making power at home may vary by co-residence with their in-laws, I employ the inverse probability treatment of weighting (IPTW) methods using propensity scores. This is done in the following steps. First, I construct propensity score models for education and co-residence respectively. Second, I calculate stabilized inverse probability weights based on propensity score models. Third, I estimate main and interaction effects of education and co-residence through regressions weighted by inverse probability weights. I will discuss each step in detail later in this section.

I choose to use IPTW methods for the following reasons. First, compared to conventional regression analyses, IPTW methods are better equipped to adjust for a large number of pretreatment covariates, are less reliant on the model specification for the outcome, and allow straightforward assessment of whether treatment and control groups overlap enough in covariates for a sensible estimation of treatment effects. Adjustment of pretreatment covariates is particularly important for this project, because living arrangement itself is a decision made by household members. Pre-existing characteristics may select women of less power into certain living arrangements. Second, compared to other propensity score methods such as matching and stratification, IPTW methods require fewer distributional assumptions about the underlying data

(Curtis et al. 2007) and are much easier to implement in the case of multiple treatments with multiple levels, as is in this project.

The outcome of this project is women’s decision making power (Y). The independent variable or main treatment is women’s education (E). There is also a moderator, whether women co-reside with their in-laws (C). Figure 1 depicts the hypothesized relations between education, co-residence, and decision making power.

Figure 1

To obtain unbiased estimates for main and interaction effects of education and co- residence, it is essential to balance baseline covariates on both education and co-residence (Dong

2015). When treatment is randomized but the moderator is not, the treatment effect for each subgroup of the moderator is still unbiased. However, it is wrong to claim that the treatment effect difference across the subgroups of the moderator is due to the moderator, because it could be due to some third factor that is correlated with both the moderator and the outcome. To illustrate this point, consider the scenario in Figure 2, in which baseline covariates (X) related to decision making power (Y) and co-residence (C) are balanced across levels of education (E), but there is an interaction between E and X on Y. The observed difference in effects of E across levels of C is not due to difference in C, but due to difference in X. Interaction between E and C is detected even when there is none in the true equation, because X is not balanced within C and

X moderates the relationship between E and Y. C itself, however, does not moderate the relationship between E and Y.

Figure 2

Similarly, consider the scenario in Figure 3, in which baseline covariates (X) and education (E) are balanced across levels of co-residence (C), but there is an interaction between

C and X on Y. Interaction between E and C is detected even when there is none in the true equation, because X is not balanced within E. C does not moderate the relationship between E and Y. Instead C moderates the relationship between X and Y, while X is correlated with E. Figure 3

Balancing on both education and co-residence can be achieved through an IPTW approach similarly to what is suggested in VanderWeele 2009. I will now describe the implementation of the method in steps.

Propensity Score Models

The first step is to construct propensity score models. Propensity score model should include all baseline covariates that are theoretically related to the outcome, even when they are not related to treatments (Rubin and Thomas 1996) and exclude all covariates that are only related to treatment assignment (Brookhard et al. 2006). The list of baseline covariates predicting the outcome of interest is generated based on extensive and systematic literature review and author’s knowledge. First, I list all variables that are theoretically hypothesized to be related to decision making power based on existing literature (Blumberg and Coleman 1989; Centers et al.

1971; Chien and Yi 2014; Conklin 1979; Malhortra and Mather 1997; Shu et al. 2012; Xu and

Lai 2002). Second, I divide these variables into pre-education pre-co-residence, post-education pre-co-residence, and post-co-residence. Pre-education and pre-co-residence covariates are included in the propensity score model for education, while post-education pre-co-residence covariates are included in the propensity score model for co-residence. Covariates that are post- co-residence, which are also post-education, are excluded, because they can be interpreted as mediators of co-residence or education and thus would not confound the relationship between education, co-residence, and outcome. Examples of such excluded covariates are proximity to parents for nuclear families and child care and housework contributions by older generations and parents’ health in co-resident families. Table 1 and Table 2 in the appendix lists all covariates used in the analyses. Figure 1 is a graphical representation of how education (E), co-residence

(C), decision making power (Y), pre-education covariates (X), and post-education pre-co- residence covariates (V) are related.

Figure 4

Propensity score model for education is thus a multinomial logistic regression of education (E) on pre-education covariates (X) related to decision making power. It can be expressed as Equation 1, where i refers to individual i, j refers to level of education, and J refers to reference level of education.

Equation 1

��� = � ��� = � + �� Propensity score model for co-residence��� = is � a logistic regression of co-residence (C) on education (E), pre-education covariates (X), and post-education and pre-co-residence covariates related to decision making power (V). It can be expressed as Equation 2. Equation 2

��� = ��� = � + ��� + �� + ��� Note that polynomial and�� cross� = product terms for some covariates are also included in the propensity score models to achieve covariate balance. A full list of these terms are available upon request.

Inverse Probability Weights

Inverse probability weights are then constructed based on propensity score model results.

Equation 3 expresses the stabilized inverse probability weights of education. Equation 4 expresses the stabilized inverse probability weights of co-residence. Equation 5 expresses the final inverse probability weights that will be used in estimating treatment effects. Note that the final weights are products of weights of education and weights of co-residence. This is essentially an application of the Bayes’ Theorem. That is P(E∩C) = P(E)*P(C|E)

Equation 3

� �� = �� � = Equation 4 �(� = �� | = �

� �(� = �� | � = �� � = Equation 5 �(� = �� | � = �� , = � , � = �

�� � � Inverse Probability Weighted Estimators � = � ∗ �

To obtain inverse probability weighted estimators, I estimate a logistic regression of whether a woman is the final decision maker on education, co-residence, and product of education and co-residence, weighted by the inverse probability weights obtained from equation

5. Equation 6 expressed this regression.

Equation 6

�� = ��� = � + ��� + ��� + ����� Note that covariates that� remain� = unbalanced after weighting are still included in the estimation model for doubly robust estimation. For clarity of presentation, these covariates are not included in equation 6.

PRELIMINARY RESULTS

Covariate Balance and Overlap of Weights

Table 1 and 2 in the Appendix shows significance of standardized differences as a measure of covariate balance before and after weighting. Except for ethnicity and region of birth, all baseline covariates are balanced across education and co-residence groups.

Figures 5 and 6 show that mean of weights across education and co-residence groups are reasonably close to 1 and have good overlap, which suggests reasonable validity for positivity assumptions.

Figure 5 Stabilized IPTW by Wife's Education Figure 6 Stabilized IPTW by Household Structure

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6 6

4 4

2 2

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Treatment Effects

Figures 7.1 through 7.5 plots predicted probability of wife being the final decision maker for each decision measured by education and co-residence, based on IPTW estimators.

Consistent with my hypotheses, in nuclear households, higher education of the wife is associated with higher probability of her being the final decision maker. In multi-generational households where the couple lives with the husband’s parents, higher education of the wife is not associated with higher probability of her being the final decision maker. In the case of expense decisions, higher education of the wife is associated with lower probability of her being the decision maker, which probably suggests gender display of power in the presence of the husband’s parents.

Figure 7.1. Wife Makes Child-Related Decisions Figure 7.2. Wife Makes Expense Decisions

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Figure 7.3. Wife Makes Financial Decisions Figure 7.4. Wife Makes Housing Decisions

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Figure 7.5. Wife Makes Luxury Purchase Decisions

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DISCUSSION

Given the increased life expectancy and growing population aging, what are? Results from this study shed light on the implications of aging societies for gender inequality. Living with husband’s parents seems to inhibit women’s ability to improve their decision making power at home through education.

References Bengtson, Vern L. 2001. “Beyond the Nuclear Family: The Increasing Importance of Multigenerational Bonds.” Journal of Marriage and Family, 63(1): 1-16. Blood, Jr., Robert O. and Donald M. Wolfe. 1960. Husbands and Wives. New York: The Free Press. Blumberg, Rae Lesser, and Marion Tolbert Coleman. 1989. “A Theoretical Look at the Gender Balance of Power in the American Couple.” Journal of Family Issues, 10(2): 225-250. Brookhart, Alan M., Sebastian Schneeweiss, Kenneth J. Rothman, Robert J. Glynn, Jerry Avorn, and Til Sturmer. 2006. American Journal of Epidemiology, 163(12): 1149-1156.

Centers, Richard, Bertram H. Raver, and Aroldo Rodrigues. 1971. “Conjugal Power Structure: A Re-Examination.” American Sociological Review 36(2): 264-278. Chen, Feinian. 2004. “The Division of Labor between Generations of Women in Rural China.” Social Science Research 33: 557-580.

Chen, Feinian, and Guangya Liu. 2009. “Population Aging in China.” International Handbook of Population Aging, 157-172. Netherlands: Springer.

Chien, Wen-Yin, and Chin-Chun Yi. 2014. “Marital Power Structure in Two Chinese Societies: Measurement and Mechanisms.” Journal of Comparative Family Studies, 45(1): 93-111. Chu, C.Y. Cyrus, Yu Xie, and Ruoh Rong Yu. 2011. “Coresidence with Elderly Parents: A Comparative Study of Southeast China and Taiwan.” Journal of Marriage and Family 73(1): 120-135.

Conklin, George H. 1979. “Cultural Determinants of Power for Women within the Family: A Neglected Aspect of Family Research.” Journal of Comparative Family Studies, 10(1): 35-53. Curtis, Lesley H., Bradley G. Hammill, Eric L. Eisenstein, Judith M. Kramer, and Kevin J. Anstrom. 2007. “Using Inverse Probability-Weighted Estimators in Comparative Effectiveness Analyses with Observational Databases.” Medical Care, 45(10S2): S103-S107. Dong, Nianbo. 2015. “Using Propensity Score Methods to Approximate Factorial Experimental Designs to Analyze the Relationship between Two Variables and an Outcome.” American Journal of Evaluation, 36(1): 42-66.

Esteve, Albert and Chia Liu. 2014. “Families in Asia: A Cross-National Comparison of Intergenerational Co-residence.” Paper presented at the IUSSP Cyber Seminar on ‘Family demography: Advancing knowledge about intergenerational relationships and exchanges’ January 9-10, 2014. Lei, Xiaoyan, John Strauss, Meng Tian, and Yaohui Zhao. 2015. “Living Arrangements of the Elderly in China: Evidnece from the CHARLS National Baseline.” China Economic Journal 8(3): 191-214. Malhotra, Anju, and Mark Mather. 1997. “Do Schooling and Work Empower Women in Developing Countries? Gender and Domestic Decisions in Sri Lanka.” Sociological Form, 12(4): 599-630.

McDonald, Gerald W. 1980. “Family Power: The Assessment of a Decade of Theory and Research, 1970-1979.” Journal of Marriage and Family, 42(4): 841-854. Rubin, Donald B., and Neal Thomas. 1996. “Matching Using Estimated Propensity Scores: Relating Theory to Practice.” Biometrics, 52(1): 249-264. Ruggles, Steven and Misty Heggeness. 2008. “Intergenerational Coresidence in Developing Countries.” Population Development Review, 34(2): 253-281. Safilios-Rothschild, Constantia. 1970. “The Study of Family Power Structure: A Review 1960- 1969.” Journal of Marriage and Family, 32(4): 539-552. Shu, Xiaoling, Yifei Zhu, and Zhanxin Zhang. 2012. “Patriarchy, Resources, and Specialization: Marital Decision-Making Power in Urban China.” Journal of Family Issues, 34(7): 885-917. VanderWeele, Tyler J. 2009. “On the Distinction between Interaction and Effect Modification.” Epidemiology, 20(6): 863-871. Whyte, Martin King. 1978. The Status of Women in Preindustrial Societies. Princeton University Press.

Wu, Xiaogang, and Zhuoni Zhang. 2010. “Changes in Educational Inequality in China, 1990- 2005: Evidence from the Population Census Data.” Research in Sociology of Education, 17: 123- 152.

Xie, Yu, and Jingwei Hu. 2014. “An Introduction to the China Family Panel Studies (CFPS).” Chinese Sociological Review, 47(1): 3-29.

Xu, Xiaohe, and Shu-Chuan Lai. 2002. “Resources, Gender Ideologies, and Marital Power, the Case of Taiwan.” Journal of Family Issues, 23(2): 209-245. Zuo, Jiping, and Ynajie Bian. 2001. “Gendered Resources, Division of Housework, and Perceived Fairness – A Case in Urban China.” Journal of Marriage and Family 63(4): 1122- 1133.

Zuo, Jiping. 2009. “Rethinking Family Patriarchy and Women’s Positions in Presocialist China.” Journal of Marriage and Family 71(3): 542-557. Table 1. Balance of Covariates of Education before and after Inverse Propensity Weighting Less than Elementary Elementary Middle School High School College + Unweighted Weighted Unweighted Weighted Unweighted Weighted Unweighted Weighted Unweighted Weighted Predictors of Wife's Education Wife's Parent's Education X *** *** *** *** *** Wife's Parent's Occupation X *** *** *** *** *** Rural Hukou X *** *** *** *** Region of birth X *** *** *** *** *** Wife's Ethnic minority X *** ** *** Wife's Age X *** ** *** *** Wife's Number of Siblings X *** *** *** *** *** standardized difference significance* p <.05 ** p<.01 *** p<.001 Table 2. Balance of Covariates before and after Inverse Propensity Weighting Predictors of Decision Making Power by Theory Balance by Living Arrangement Unweighted Weighted Socioeconomic Resources (Classic Resource Theory) Wife's education Husband's education * Wife's occupation Husband's occupation Wife's Individual income Husband's individual income Household income * Wife's Parent's Education *** Wife's Parent's Occupation * Husband's Parent's Education *** Husband's Parent's Occupation * Wife is a owner of the house *** Husband is a owner of the house *** Dowry in 2010 constant yuan *** Cultural Resources (Modified Resource Theory) Wife's self-identified social class (1 -5, 1 lowest) Husband's self-identified social class (1-5, 1 lowest) Wife's personal gender ideology Men should focus on career; women should focus on family (1-5, 1 strongly disagree) A good marriage is better for women than a good career (1-5, 1 strongly disagree) A woman is only complete if she has children (1-5, 1 strongly disagree) Men should do half the housework (1-5, 1 strongly disagree) Son should live with parents after marriage (1-5, 1 strongly disagree) *** Ideal number of children Husband's personal gender ideology Men should focus on career; women should focus on family (1-5, 1 strongly disagree) A good marriage is better for women than a good career (1-5, 1 strongly disagree) A woman is only complete if she has children (1-5, 1 strongly disagree) Men should do half the housework (1-5, 1 strongly disagree) Son should live with parents after marriage (1-5, 1 strongly disagree) *** Ideal number of children Parents Arranged Marriage Rural Hukou *** Region of birth *** Urban place of residence *** Wife's Province of residence different from that of birth Husband's province of residence different from that of birth ** Wife's Ethnic minority *** Husband's Ethnic minority Wife's Age *** Wife's Age minus Husband's Age Other Family Resources Wife's Number of Siblings *** Husband's number of siblings *** Number of children under age 6 *** Wife's has at least one alive parent *** standardized difference significance* p <.05 ** p<.01 *** p<.001