Abstract
Using the 2010 China Family Panel Studies data, this article provides new evidence on the gender gap and the role of education in time use among married Chinese couples in both urban and rural areas. Across urban and rural areas and on both work and non-work days, wives spent much more time on personal and household care, while husbands spent more time on work and leisure/social activities. For urban wives, having the same or higher levels of education than their husbands helped narrow the gender gap in time spent on personal and household care, paid work, and leisure/social activities. In contrast, rural wives with the same levels of education as their husbands increased their time spent on leisure and social activities, but they also increased their time spent on personal and household care. These results help shed light on policy debates regarding gender inequality in China and around the world.
Keywords: time use, gender gap, education, couples, China
Introduction
Motivation
A growing body of literature has documented the persistent gender inequality in the labor market and beyond. Substantial gender wage gaps exist across developed and developing countries, despite some recent progress in narrowing such gaps (Winter-Ebmer & Weichselbaumer, 2005). Alongside the gender inequality in the labor market is the equally persistent and widespread, but much less understood, gender inequality at the home front. Since the 1960s, women in most developed countries have substantially increased their labor force participation, yet little has changed in their time spent on unpaid household work (Bonke, 1995; Bonke & Jensen, 2012; Dong & An, 2015; Gershuny, 2000; Gimenes-Nadal & Sevilla, 2012; Kan, Sullivan, & Gershuny, 2011; Qi & Dong, 2018; Slaughter, 2015). This body of literature shows that education is a crucial determinant for the convergence of men and women’s time spent on paid and unpaid work. Women with a higher level of education are more likely to participate in the labor market and reduce their time spent on unpaid household work (Bonke, 1995; Gershuny, 2000). In other words, education helps narrow the gender gap in unpaid work, but does not eliminate it.
China offers a unique yet understudied case in this growing international literature. On the one hand, embodied by the slogan ‘women hold up half the sky,’ Chinese women have had high labor force participation rates since the establishment of socialist China in 1949. In 2012, the female-to-male labor force participation rate in China was 0.82, which ranked among the most developed countries (International Labour Organization, 2013). On the other hand, there is pervasive overt and covert gender discrimination in nearly every aspect of the Chinese society. For example, male preference is often openly stated in hiring advertisements, and for many humanity and social science majors, male students enjoy lower admission standards than their female peers in the national college entrance exam. Moreover, Chinese women remain the primary family caregivers, not only for their children and older parents, but often for their husbands’ older parents (Hong-Fincher, 2014; Liu, 2011). As a result, gender inequality in work and non-work time is still a pervasive phenomenon in the Chinese society (Dong & An, 2015; Qi & Dong, 2018).
Summary of Prior Literature
Alongside the growing literature on gender wage gaps (Li & Song, 2015; Li, Song, & Liu, 2014; Liu, Sicular, & Xin, 2006; Song, Sicular, & Gustafsson, 2016), a recent set of studies have investigated the gender gap in unpaid work and time use patterns in China (Cook & Dong, 2011; Dong & Li, 2011; Dong & Zhang, 2009; Qi & Dong, 2018). In particular, using 1991–2006 data from the China Health and Nutrition Survey (CHNS), Chang, MacPhail, and Dong (2011) found that women’s share of paid and unpaid work time increased in both the farm and off-farm sectors in rural China, and that economic development helped accelerate the rise in total work time but not an increase in the time use gender gap.
Using data from the 2008 China Time Use Survey (CTUS), Dong and An (2015) found that, while women had higher total paid and unpaid work time than men in both rural and urban sectors (by 7 and 10.5 hours per week, respectively), the female-male gap in unpaid work time was much larger than male-female gap in paid work (16.7 vs. 11.3 hours per week). Marriage appeared to reinforce the gender gap in time allocation in that it increased both total work time and unpaid work time more for women than for men, which resulted in a greater reduction in women’s non-work time (e.g., time for self-care and leisure) (Dong & An, 2015). Meanwhile, gender difference was found in the roles of education in time use. Among women, education was negatively associated with paid and unpaid work time and positively with non-work time; among men, education was negatively associated with paid work time and positively with non-work time but had no significant association with unpaid work time (Dong & An, 2015).
In a recent study, Qi and Dong (2018) used data from 2008 CTUS and 2008 China Household Income Project (CHIP) to investigate gender gaps in time poverty (defined as lack of enough time for rest and leisure) in urban China. They found that women paid workers and low-paid workers accounted for a disproportionate share of the time poor. Being married was significantly associated with time poverty for both men and women. However, women with higher education were more likely to be time poor, but the same effect was not found for men (Qi & Dong, 2018).
Contributions
Building on this body of research on gender difference in time use patterns in China, especially on the roles of marriage and education, this study focuses on the gender gap in time use among married Chinese couples and whether education can help narrow the gender gap across the urban-rural settings. Due to the total time constraint and the tradeoff among the time allocated for paid work, unpaid work, and non-work activities, two main perspectives have been adopted to understand the exchange process between resources (e.g., earnings, occupational prestige, and education) and time allocation among married couples (Carlson & Lynch, 2017; Dong & An, 2015; Qi & Dong, 2018). On the one hand, the relative resources or bargaining perspective suggests that the spouse with the most resources can bargain with his/her spouse to reduce the responsibility of housework, which can lead to a more equitable share of housework, as research shows that wives with relatively equal earnings do less housework and husbands do more (Bittman et al., 2003; Carlson & Lynch, 2017; Greenstein, 2000). On the other hand, the gender deviance neutralization or compensatory gender display perspective, supported by evidence from cross-national studies, suggests that women who substantially out-earn their husbands compensate or demonstrate traditional gender-related behaviors on femininity by over-performing housework, whereas husbands display their masculinity by refusing housework (Bittman et al., 2003; Carlson & Lynch, 2017; Greenstein, 2000; Hochschild, 2012; Hook, 2017). Meanwhile, education inequalities, especially the widening gaps in higher education between urban and rural areas, have been increasing in the past several decades (Lei & Shen, 2015). Most studies within the framework of bargaining or compensatory gender display perspective have focused on couples’ earnings and occupational prestige; while few have directly examined the role of education in the gender gap in time allocation, especially in the cultural and social contexts of China including the substantial differences in couples’ educational attainment and daily activities across the urban-rural divide.
In this article, we investigate the gender gap in time use across the urban-rural divide among Chinese married couples using a large national dataset. We restrict the sample to couples to compare time use patterns within husband-wife dyads to understand the allocation of time for paid and unpaid work as well as leisure and social activities. Focusing on the possible role of couples’ education level difference, we examine whether having an equal or higher education level than her husband helps empower the wife to reduce time spent on unpaid work and narrow the gender gap in time use. Utilizing the detailed time use data and limiting the sample to couples help us provide unprecedented empirical evidence on the gender gap in time use and the possible role of education in narrowing such gaps in China and contribute to the growing international literature on this important topic. Based on the literature, as reviewed above, we hypothesize that there are different patterns in the associations between couples’ education level difference and their time use across the urban-rural divide. While wives’ education can help them narrow the gaps in time use in unpaid work, they may also show traditional gender-related behaviors and spend more time on certain unpaid activities, especially among those in rural areas.
Data and Methods
Sample
This article uses data from the 2010 baseline survey of the China Family Panel Studies (CFPS). Launched in 2010, CFPS is a biannual longitudinal survey conducted by the Institute of Social Science Survey (ISSS) of Peking University. The 2010 CFPS sample is representative of 95% of the Chinese population, including 25 provinces and municipalities. Collecting data at individual, family, and community levels, CFPS consists of five datasets: community, family roster, family, adults, and children. This study uses data primarily from the adult dataset, which includes individual characteristics and detailed time use data of each family member of age 16 and older, with household level variables merged in from the family and family roster datasets. Data from CFPS 2012, 2014, and 2016 surveys are also available, but they do not contain detailed questions on time use and thus cannot be used for this study.
To capture the differences in couples’ education levels and time use, this study uses each husband-wife dyad as the unit of analysis. The original adult dataset included 33,600 respondents from 14,068 households. Among them, 52% were female and 48% were male. Among respondents whose spouses were living in the same household at the time of the interview, the spouses were also interviewed and included in the adult dataset (CFPS excluded cohabiting partners from the interview). After merging in household level variables from the family and family roster datasets, our study sample comprises 11,354 matched couples (or 22,708 adults) without missing data on education levels and time use, including 5,243 couples in urban areas and 6,111 couples in rural areas. To understand the different patterns in gender gap in time use and also take into account educational inequalities across urban and rural areas, all analyses are carried out among the respective urban and rural samples.
Measures of Time Use
The dependent variable, gender gap in time use, is measured by the difference in time spent on certain activities between the couple, calculated as the wife’s time spent on that activity minus that of the husband. Times were reported in hours and minutes in CFPS and converted into hours for analyses in this study. The gender gap in couples’ time use therefore has a possible range between −24 and 24 hours, with positive values indicating wives spending more time than husbands and negative values indicating vice versa.
In the 2010 CFPS, all respondents were asked to report the average time spent on various activities on a typical work day and non-work day (as defined by each respondent according to his/her own schedule) during the past non-vacation month. These activities include seven major categories: 1) personal and household care, 2) paid work, 3) leisure and social activities, 4) education activities, 5) transportation, 6) other activities, and 7) idle time (no specified activity).
Some of the major categories of activities are further divided to more detailed subcategories. Specifically, personal and household care activities include 1) sleeping and resting, 2) eating and drinking, 3) personal hygiene, 4) housekeeping, and 5) care for family members. Paid work activities include full-time and part-time work. Leisure and social activities include 1) reading traditional media (e.g., recreational magazines and newspapers), 2) watching TV or listening to radio or music, 3) using the Internet for recreation, 4) exercise and other leisure activities including sports and physical exercise, 5) participating in hobbies, games, and other recreational playing activities, 6) social networking, 7) community and public voluntary services, and 8) religious activities. Education activities include 1) formal education, 2) study activities related to formal education such as completing assignments or reviewing course materials, and 3) informal education or training.
It is important to note that this measure of time use is based on individuals’ self-definition of typical work and non-work days and recalls of their time allocations for two 24-hour periods. This is different from how most more established time use surveys are conducted, where respondents are asked to provide a 24-hour time diary, noting the start and end times of each activity and the day of the week for the diary. Because of this difference, the time use data in this study may suffer from issues of measurement and recall biases. Future research adopting the more established time use survey method can help address this limitation. Currently, the time use data used in this study is one of the very few that exist and are publicly available. These data allow us a rare opportunity to look into the nuanced time use patterns of Chinese couples and gauge the possible influence of the difference in their education levels.
Measures of Gender Gap in Education and Other Independent Variables
The key independent variable is couples’ difference in education levels. Based on the categories specified in the questionnaire of CFPS, education is measured at five levels, including illiterate/semi-literate, elementary school, junior middle school, high school, and some college or higher education. The gender gap in couples’ education levels is captured by comparing wife’s and husband’s education levels. This comparison results in three different situations: 1) wives have lower levels of education than husbands, which is the ‘norm’ according to traditional gender role expectations in China and used as the reference group in the regression analyses; 2) wives and husbands have same levels of education, the increasingly prevalent scenario in today’s China, especially in urban areas; and 3) wives have higher levels of education, a non-traditional but growing trend, especially in urban areas. We expect that the higher education levels of wives give them more bargaining power and enable them to reduce their time spent on unpaid household work and narrow the gender gap in time use.
It is important to note that this indicator of difference in couples’ education levels only measures whether a couple has the same or different levels of education rather than the exact differences in education levels (e.g., the difference between illiterate and high school would be coded the same as that between illiterate and elementary school). The specific education levels of both respondents and their spouses are also included in the regression models to take into account their potential confounding roles in the relationship between couples’ education levels and gender gap in time use.
In addition, following the most relevant existing literature (Chang, MacPhail, & Dong, 2011; Dong & An, 2015; Qi & Dong, 2018), we control for a rich array of individual and household characteristics in all regressions. Specifically, individual characteristics include respondents’ and their spouses’ age, ethnic minority status (yes/no), Communist Party membership (yes/no), employment status (yes/no), individual annual income (i.e., the sum of job-related earnings, bonuses, and other benefits; coded into five categories: no income, 1–5,000 yuan, 5,001–15,000 yuan, 15,001–30,000 yuan, and above 30,000 yuan), self-perceived health status (healthy, fair, relatively unhealthy, not healthy, and very unhealthy), and self-reported depression scores. Household characteristics include the number of children in household in specific age groups (0–5, 6–12, and 13–15 years old), the number of older persons in specific age groups (60–70, and 71 years or older), whether family had at least one member with chronic disease (yes/no), and whether family had at least one member with severe disease (yes/no).
Analytic Strategy
We first present descriptive statistics of the time use patterns of wives and husbands and present their differences, or the gender gap in time use. We do so for urban and rural samples separately, and within each sample, for work and non-work days separately. We then conduct ordinary least squares (OLS) regressions to examine whether couples’ difference in education levels plays a role in helping narrow the gender gap in time use. Specifically, we investigate whether having the same or a higher level of education than her husband helps a Chinese wife reduce her time spent on unpaid household work and narrow the gender gap in time use with her husband. To take into account the potential influences of confounding factors, we follow the relevant literature and control for wife’s and husband’s respective education levels, employment status, individual annual income, and other individual and household characteristics. To account for the observed and unobserved homogeneity across communities (i.e., villages in rural areas and sub-districts, or jiedao, in urban areas), we include community fixed effects in all regressions and cluster robust standard errors at the community level. All descriptive and regression analyses are adjusted by sampling weights provided in the CFPS data.
The analyses are conducted first in the full sample with the interactions between couples’ education level difference and the urban-rural location and to examine whether the associations between couples’ education level difference and their time use gaps are significantly different across the urban-rural divide. We then analyze the associations between couples’ education level difference and their time use gaps in the respective urban and rural areas. The gender gaps in time use on major categories of activities are analyzed first, followed by those on more detailed sub-categories.
Results
Sample Characteristics
Table 1 presents the demographic and socioeconomic characteristics of the Chinese couples among the full sample and by urban-rural subsamples after adjusting for sampling weights. Regression models (OLS for continuous measures and logistic regressions for binary measures) with sampling weights are used to test the mean differences between urban and rural subsamples, with significant levels indicated to the statistics in the rural subsample. The top panel shows the key independent variable, couple’s education level difference. In the national full sample, 43% couples had the same levels of education, followed closely by 42% of wives having lower levels of education than their husbands. Only 15% of wives had higher levels of education than their husbands. These patterns are largely mirrored in both urban and rural areas, with some differences. As expected, in urban areas, more wives had higher levels of education than their husbands (18%) as compared to their rural peers (13%), whereas a higher proportion of rural wives had lower levels of education than their husbands (45%) as compared to their urban peers (38%). The proportion of couples with the same level of education was largely consistent across the two areas (44% for urban and 42% for rural areas).
Table 1.
Descriptive statistics in full sample and in urban and rural subsamples
| Full Sample (N = 11,354 couples) | Urban (N = 5,243 couples) | Rural (N = 6,111 couples) | |
|---|---|---|---|
| Couple’s Education Level Difference | |||
| Wife education lower than husband | 0.42 (0.49) | 0.38 (0.49) | 0.45 (0.50)** |
| Same education | 0.43 (0.50) | 0.44 (0.50) | 0.42 (0.49) |
| Wife education higher than husband | 0.15 (0.36) | 0.18 (0.38) | 0.13 (0.33)** |
| Wife Characteristics | |||
| Age | 46.73 (13.23) | 46.89 (13.34) | 46.54 (13.10) |
| Ethnic minority (yes/no) | 0.09 (0.29) | 0.06 (0.24) | 0.13 (0.33)** |
| Communist Party member (yes/no) | 0.04 (0.20) | 0.07 (0.25) | 0.01 (0.09)** |
| Education | |||
| Illiterate | 0.34 (0.47) | 0.21 (0.41) | 0.50 (0.50)** |
| Elementary school | 0.19 (0.39) | 0.16 (0.37) | 0.24 (0.42)** |
| Junior middle school | 0.28 (0.45) | 0.33 (0.47) | 0.21 (0.41)** |
| High school | 0.12 (0.32) | 0.18 (0.39) | 0.04 (0.19)** |
| Some college or higher | 0.07 (0.26) | 0.12 (0.33) | 0.01 (0.10)** |
| Employed (yes/no) | 0.43 (0.49) | 0.38 (0.49) | 0.49 (0.50)** |
| Individual annual income | |||
| No income | 0.39 (0.49) | 0.36 (0.48) | 0.44 (0.50)** |
| 1–5,000 yuan | 0.26 (0.44) | 0.18 (0.39) | 0.36 (0.48)** |
| 5,001–15,000 yuan | 0.22 (0.42) | 0.27 (0.45) | 0.16 (0.36)** |
| 15,001–30,000 yuan | 0.10 (0.29) | 0.14 (0.34) | 0.03 (0.18)** |
| Above 30,000 yuan | 0.03 (0.16) | 0.05 (0.21) | 0.01 (0.07)** |
| Self-perceived health status | |||
| Healthy | 0.41 (0.49) | 0.43 (0.50) | 0.39 (0.49)** |
| Fair | 0.41 (0.49) | 0.43 (0.50) | 0.38 (0.49)** |
| Relatively unhealthy | 0.07 (0.25) | 0.06 (0.24) | 0.07 (0.26) |
| Unhealthy | 0.09 (0.29) | 0.06 (0.24) | 0.13 (0.34)** |
| Very unhealthy | 0.02 (0.13) | 0.01 (0.11) | 0.02 (0.16)** |
| Depression score (range of 6–30) | 27.04 (3.72) | 27.36 (3.50) | 26.64 (3.93)** |
| Husband Characteristics | |||
| Age | 47.33 (13.33) | 47.59 (13.48) | 47.00 (13.13) |
| Ethnic minority (yes/no) | 0.08 (0.28) | 0.05 (0.22) | 0.12 (0.33)** |
| Communist Party member (yes/no) | 0.07 (0.25) | 0.10 (0.30) | 0.03 (0.17)** |
| Education | |||
| Illiterate | 0.32 (0.47) | 0.19 (0.40) | 0.48 (0.50)** |
| Elementary school | 0.19 (0.39) | 0.16 (0.36) | 0.24 (0.43)** |
| Junior middle school | 0.29 (0.45) | 0.35 (0.48) | 0.22 (0.42)** |
| High school | 0.12 (0.33) | 0.18 (0.38) | 0.05 (0.22)** |
| Some college or higher | 0.07 (0.26) | 0.12 (0.33) | 0.01 (0.10)** |
| Employed (yes/no) | 0.49 (0.50) | 0.46 (0.50) | 0.52 (0.50)** |
| Individual annual income | |||
| No income | 0.17 (0.38) | 0.17 (0.38) | 0.17 (0.38) |
| 1–5,000 yuan | 0.21 (0.40) | 0.12 (0.33) | 0.30 (0.46)** |
| 5,001–15,000 yuan | 0.32 (0.47) | 0.31 (0.46) | 0.32 (0.47) |
| 15,001–30,000 yuan | 0.22 (0.42) | 0.27 (0.44) | 0.16 (0.37)** |
| Above 30,000 yuan | 0.08 (0.28) | 0.13 (0.33) | 0.03 (0.18)** |
| Self-perceived health status | |||
| Healthy | 0.45 (0.50) | 0.46 (0.50) | 0.42 (0.49)* |
| Fair | 0.39 (0.49) | 0.41 (0.49) | 0.37 (0.48)* |
| Relatively unhealthy | 0.06 (0.24) | 0.06 (0.23) | 0.07 (0.25) |
| Not healthy | 0.08 (0.28) | 0.06 (0.24) | 0.11 (0.31)** |
| Very unhealthy | 0.02 (0.12) | 0.01 (0.10) | 0.02 (0.15)** |
| Depression score (range of 6–30) | 27.26 (3.65) | 27.56 (3.47) | 26.90 (3.84)** |
| Household Characteristics | |||
| Urban (yes/no) | 0.55 (0.50) | 1.00 (0.00) | 0.00 (0.00) |
| Number of children in household | |||
| 0–5 years old | 0.31 (0.58) | 0.26 (0.52) | 0.38 (0.65)** |
| 6–12 years old | 0.33 (0.57) | 0.28 (0.49) | 0.40 (0.65)** |
| 13–15 years old | 0.19 (0.44) | 0.16 (0.40) | 0.22 (0.49)** |
| Number of elders in household | |||
| 60–70 years old | 0.38 (0.69) | 0.37 (0.68) | 0.40 (0.71)+ |
| 71 years or older | 0.26 (0.58) | 0.28 (0.60) | 0.25 (0.56) |
| At least one member with chronic disease | 0.29 (0.46) | 0.30 (0.46) | 0.29 (0.45) |
| At least one member with severe disease | 0.18 (0.38) | 0.15 (0.36) | 0.22 (0.41)** |
Notes: Means with standard deviations in parentheses presented in the full sample as well as the urban and rural subsamples are adjusted for sampling weights; regression models (OLS for continuous measures and logistic regressions for binary measures) with sampling weights are used to test the mean differences between urban and rural subsamples, with significant levels indicated to the statistics in the rural subsample:
p < 0.01
p < 0.05
p < 0.10.
The rest of Table 1 presents the individual characteristics of wives and husbands, as well as their household characteristics. Consistent with previous studies using 2010 CFPS data (e.g., Lei & Shen, 2015; Ma & Rizzi, 2017; Xu & Xie, 2017), the educational attainment of individuals in the sample was relatively low (with a high illiteracy rate) compared to the national statistics even after applying weights. Specifically, among wives, 34% in the full sample were illiterate or semi-literate, 19% had elementary school education, 28% had junior middle school, 12% had high school, and 7% had some college or higher education. Overall, rural wives had lower educational attainment than urban wives (e.g., with an illiterate rate of 50% and 21%, respectively). Similar distribution of husbands’ education levels existed in the full sample as well as the respective rural and urban subsamples. In terms of other characteristics, among wives, the average age was 47 years old in both urban and rural areas. A greater proportion of rural wives were ethnic minorities (13%), employed (49%), and unhealthy or very unhealthy (15%), as compared to urban wives (6%, 38%, and 7%, respectively). Urban wives tended to have higher individual income than rural wives (e.g., 36% with no income and 19% with above 15,000 yuan, compared to 44% and 4%, respectively, among rural wives). Urban wives were also more likely to be Communist Party members (7%) and less likely to be depressed (with an average score of 27.36, in the range of 6–30, with higher scores indicating less depression), compared to their rural peers (1% and 26.64, respectively). Similar urban-rural differences existed among husbands as well, but husbands in general were more privileged than their wives in terms of Communist Party membership, employment status, individual income, and health status across urban and rural areas.
Regarding household characteristics, rural couples had more children across age groups than urban couples (0.38 aged 0–5, 0.40 aged 6–12, and 0.22 aged 13–15, as compared to 0.26, 0.28, and 0.16, respectively). Rural couples had slightly more elders between the ages 60–70 living with them (0.40) than their urban peers (0.37), but not elders aged 71 or older (0.25 in rural areas vs. 0.28 in urban areas). In addition, a greater proportion of rural households had at least one member with severe disease (22%) as compared to urban households (15%).
Gender Gap in Time Use among Chinese Couples
Table 2 presents the descriptive results for wives, husbands, and their differences in time use in specific activities, using OLS regressions to test the statistical significance in their differences, both adjusted for sampling weights. Across urban and rural areas and on both work and non-work days, wives spent much more time on personal and household care, especially on housekeeping and taking care of family members, while husbands spent significantly more time on work and leisure/social activities. Specifically, among urban couples, wives on average spent 2.58 more hours than their husbands on personal and household care than their husbands on a typical work day and 2.55 more hours on a typical non-work day. Over half of this extra time was spent on housekeeping (1.48 more hours on work days and 1.53 more hours on non-work days than their husbands). The next biggest difference in this category was from caring for family members, with wives spending 0.84 more hours on both work and non-work days than their husbands. Wives also spent more time on personal hygiene (by 0.18 hours on work days and 0.17 hours on non-work days) than their husbands.
Table 2.
Couples’ time use and differences in major and sub-categories of activities (in hours)
| Urban Sample (N = 5,243 couples) |
Rural Sample (N = 6,111 couples) |
|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Work Days |
Non-work Days |
Work Days |
Non-work Days |
|||||||||
| Wife | Husband | Diff | Wife | Husband | Diff | Wife | Husband | Diff | Wife | Husband | Diff | |
| Personal and Household Care | 14.15 | 11.58 | 2.58** | 14.90 | 12.35 | 2.55** | 14.88 | 12.09 | 2.79** | 15.39 | 12.76 | 2.63** |
| (3.23) | (2.61) | (0.10) | (3.25) | (2.81) | (0.10) | (3.29) | (2.85) | (0.09) | (3.37) | (3.03) | (0.10) | |
| Sleeping & resting | 7.92 | 7.86 | 0.06 | 8.16 | 8.21 | −0.04 | 8.37 | 8.21 | 0.15** | 8.57 | 8.56 | 0.02 |
| (1.42) | (1.48) | (0.05) | (1.59) | (1.67) | (0.05) | (1.57) | (1.65) | (0.05) | (1.58) | (1.65) | (0.05) | |
| Eating & drinking | 1.39 | 1.37 | 0.02 | 1.43 | 1.38 | 0.05* | 1.54 | 1.48 | 0.07** | 1.56 | 1.50 | 0.06* |
| (0.70) | (0.67) | (0.02) | (0.71) | (0.71) | (0.02) | (0.84) | (0.77) | (0.02) | (0.82) | (0.78) | (0.02) | |
| Personal hygiene | 0.97 | 0.79 | 0.18** | 1.02 | 0.85 | 0.17** | 0.94 | 0.78 | 0.16** | 0.98 | 0.81 | 0.16** |
| (0.54) | (0.40) | (0.02) | (0.54) | (0.44) | (0.02) | (0.55) | (0.46) | (0.02) | (0.56) | (0.46) | (0.01) | |
| Housekeeping | 2.36 | 0.88 | 1.48** | 2.59 | 1.05 | 1.53** | 2.60 | 1.01 | 1.59** | 2.79 | 1.21 | 1.58** |
| (1.67) | (1.13) | (0.05) | (1.69) | (1.21) | (0.05) | (1.63) | (1.41) | (0.04) | (1.66) | (1.50) | (0.05) | |
| Care for family members | 1.57 | 0.72 | 0.84** | 1.74 | 0.90 | 0.84** | 1.50 | 0.62 | 0.88** | 1.62 | 0.76 | 0.86** |
| (2.13) | (1.32) | (0.06) | (2.28) | (1.57) | (0.07) | (2.19) | (1.19) | (0.05) | (2.22) | (1.31) | (0.05) | |
| Paid Work | 3.59 | 5.62 | −2.04** | 1.66 | 2.79 | −1.13** | 4.06 | 6.44 | −2.38** | 2.65 | 4.21 | −1.55** |
| (4.13) | (4.39) | (0.14) | (3.28) | (4.19) | (0.12) | (3.97) | (4.08) | (0.12) | (3.56) | (4.19) | (0.12) | |
| Full-time work | 3.48 | 5.49 | −2.02** | 1.52 | 2.62 | −1.11** | 3.96 | 6.16 | −2.19** | 2.52 | 3.83 | −1.31** |
| (4.11) | (4.39) | (0.14) | (3.19) | (4.12) | (0.12) | (3.95) | (4.12) | (0.12) | (3.50) | (4.14) | (0.11) | |
| Part-time work | 0.11 | 0.15 | −0.03 | 0.14 | 0.17 | −0.03 | 0.10 | 0.28 | −0.18** | 0.14 | 0.39 | −0.25** |
| (0.85) | (1.05) | (0.03) | (0.97) | (1.10) | (0.03) | (0.76) | (1.40) | (0.04) | (0.95) | (1.64) | (0.05) | |
| Leisure and Social Activities | 3.97 | 4.52 | −0.54** | 4.74 | 5.86 | −1.11** | 2.87 | 3.14 | −0.27** | 3.40 | 4.04 | −0.64** |
| (2.63) | (2.83) | (0.09) | (2.78) | (3.27) | (0.10) | (2.25) | (2.31) | (0.07) | (2.50) | (2.77) | (0.08) | |
| Reading traditional media | 0.34 | 0.53 | −0.18** | 0.37 | 0.60 | −0.22** | 0.08 | 0.17 | −0.10** | 0.09 | 0.21 | −0.12** |
| (0.64) | (0.81) | (0.03) | (0.69) | (0.88) | (0.03) | (0.32) | (0.46) | (0.01) | (0.35) | (0.53) | (0.01) | |
| Watching TV or listening to radio/music | 1.87 | 1.91 | −0.04 | 2.18 | 2.36 | −0.18** | 1.57 | 1.65 | −0.08* | 1.81 | 2.01 | −0.21** |
| (1.39) | (1.56) | (0.05) | (1.45) | (1.73) | (0.05) | (1.26) | (1.23) | (0.04) | (1.36) | (1.45) | (0.04) | |
| Using Internet for recreation | 0.25 | 0.38 | −0.13** | 0.32 | 0.52 | −0.20** | 0.04 | 0.07 | −0.04** | 0.04 | 0.09 | −0.05** |
| (0.74) | (0.96) | (0.03) | (0.91) | (1.23) | (0.04) | (0.28) | (0.44) | (0.01) | (0.33) | (0.54) | (0.01) | |
| Exercise | 0.50 | 0.55 | −0.05 | 0.54 | 0.66 | −0.12** | 0.11 | 0.15 | −0.04* | 0.12 | 0.16 | −0.04* |
| (0.81) | (0.87) | (0.03) | (0.87) | (1.10) | (0.04) | (0.46) | (0.49) | (0.02) | (0.46) | (0.49) | (0.02) | |
| Hobbies, games, and recreational playing | 0.37 | 0.48 | −0.11** | 0.55 | 0.79 | −0.23** | 0.31 | 0.30 | 0.01 | 0.44 | 0.58 | −0.14** |
| (0.93) | (1.03) | (0.03) | (1.10) | (1.36) | (0.04) | (0.87) | (0.81) | (0.03) | (1.07) | (1.24) | (0.04) | |
| Social networking | 0.57 | 0.62 | −0.05+ | 0.70 | 0.87 | −0.17** | 0.74 | 0.77 | −0.03 | 0.89 | 0.98 | −0.10** |
| (0.87) | (0.89) | (0.03) | (0.98) | (1.12) | (0.03) | (1.03) | (1.08) | (0.03) | (1.13) | (1.23) | (0.04) | |
| Community & public services | 0.03 | 0.04 | −0.02 | 0.02 | 0.05 | −0.02* | 0.01 | 0.01 | 0.00 | 0.01 | 0.01 | −0.01** |
| (0.21) | (0.39) | (0.01) | (0.18) | (0.40) | (0.01) | (0.13) | (0.16) | (0.00) | (0.09) | (0.16) | (0.00) | |
| Religious activities | 0.04 | 0.01 | 0.03** | 0.06 | 0.02 | 0.04** | 0.02 | 0.01 | 0.01* | 0.02 | 0.01 | 0.01+ |
| (0.29) | (0.16) | (0.01) | (0.38) | (0.24) | (0.01) | (0.22) | (0.10) | (0.01) | (0.24) | (0.17) | (0.01) | |
| Education Activities | 0.03 | 0.04 | −0.01 | 0.05 | 0.05 | −0.00 | 0.01 | 0.02 | −0.01 | 0.00 | 0.02 | −0.01** |
| (0.26) | (0.36) | (0.01) | (0.45) | (0.45) | (0.02) | (0.19) | (0.22) | (0.01) | (0.11) | (0.23) | (0.00) | |
| Formal education | 0.01 | 0.01 | −0.00 | 0.02 | 0.02 | −0.00 | 0.00 | 0.01 | −0.01* | 0.00 | 0.01 | −0.01* |
| (0.14) | (0.28) | (0.01) | (0.32) | (0.35) | (0.01) | (0.03) | (0.14) | (0.00) | (0.02) | (0.10) | (0.00) | |
| Study related to formal education | 0.01 | 0.01 | 0.00 | 0.01 | 0.01 | 0.00 | 0.00 | 0.00 | −0.00 | 0.00 | 0.00 | −0.00 |
| (0.11) | (0.11) | (0.00) | (0.16) | (0.09) | (0.00) | (0.04) | (0.06) | (0.00) | (0.02) | (0.07) | (0.00) | |
| Informal education or training | 0.01 | 0.02 | −0.01 | 0.01 | 0.02 | −0.01 | 0.01 | 0.01 | −0.00 | 0.00 | 0.01 | −0.01* |
| (0.13) | (0.17) | (0.00) | (0.16) | (0.24) | (0.01) | (0.16) | (0.14) | (0.00) | (0.11) | (0.16) | (0.00) | |
| Transportation | 0.48 | 0.57 | −0.09** | 0.41 | 0.47 | −0.05* | 0.38 | 0.53 | −0.16** | 0.34 | 0.49 | −0.15** |
| (0.66) | (0.69) | (0.02) | (0.64) | (0.68) | (0.02) | (0.60) | (0.73) | (0.02) | (0.59) | (0.73) | (0.02) | |
| Other Activities | 0.74 | 0.66 | 0.08 | 0.83 | 0.85 | −0.02 | 0.83 | 0.84 | −0.01 | 0.97 | 1.11 | −0.15* |
| (1.90) | (1.88) | (0.06) | (1.97) | (2.05) | (0.07) | (1.88) | (2.03) | (0.05) | (2.10) | (2.38) | (0.06) | |
| Idle Time | 0.93 | 0.92 | 0.00 | 1.10 | 1.20 | −0.11 | 0.74 | 0.72 | 0.02 | 0.93 | 1.01 | −0.08 |
| (2.34) | (2.30) | (0.09) | (2.51) | (2.62) | (0.10) | (1.79) | (1.86) | (0.05) | (2.10) | (2.24) | (0.06) | |
Notes: Means with standard deviations in parentheses, adjusted by sampling weights, are presented in table for wives, husbands, and their differences in time use in specific activities; OLS regressions with sampling weights are used to test the statistical significance in their differences:
p < 0.01
p < 0.05
p < 0.10.
By contrast, urban husbands spent more time on both paid work and leisure/social activities than their wives. On average, urban husbands spent 2.04 more hours on paid work on typical work days and 1.13 more hours on non-work days than their wives. Among these, the majority (99% on work days and 98% on non-work days) was spent on full-time work, with the remaining spent on part-time work. Urban husbands also spent more time on leisure and social activities on both work (by 0.54 hours) and non-work days (by 1.11 hours) than their wives, including on reading traditional media; using Internet for recreation; exercise; hobbies, games, and recreational playing; social networking; and community and public services. The only exception was that wives spent slightly more time on religious activities (by 0.03 hours on work days and 0.04 hours on non-work days) than their husbands. Related to the greater time spent on work and leisure/social activities, urban husbands also spent more time on transportation (by 0.09 hours on work days and 0.05 hours on non-work days) than their wives. Urban wives and husbands had no significantly different time use on education activities, other non-specified activities, or and idle time.
These patterns held very consistently among rural couples, as is evident in Table 2. Similar to their urban peers, rural wives spent much more time on personal and household care and much less time on paid work as well as leisure/social activities than their husbands, on both work and non-work days. The majority of the extra time spent on personal and household care by rural wives was also on housekeeping and care for family members. Rural wives also spent less time on almost all leisure and social activities than their husbands except for religious activities.
Does Education Help Narrow the Gender Gap in Time Use?
Table 3 presents the OLS regression results on couples’ education level difference and the gender gap in time use in major categories of activities after adjusting for sampling weights and controlling for wife, husband, and household characteristics, with robust standard errors clustered at community level. The results on time use gap in sub-categories with significant findings are presented in Appendix Table 1. Overall, for urban wives, having the same or higher levels of education than their husbands helped narrow the gender gap in time spent on personal and household care, paid work, and leisure/social activities; whereas in rural areas, the role of education in helping narrow the gender gap in time use among couples was mixed. The discussion below focuses on the findings that were statistically significant at p < 0.05 or marginally significant at p < 0.10.
Table 3.
Regression results on couples’ education level difference and time use gap in major categories of activities
| Work Days |
Non-work Days |
|||||
|---|---|---|---|---|---|---|
| Full | Urban | Rural | Full | Urban | Rural | |
| Personal and Household Care | ||||||
| Couples’ education (wife lower omitted) | ||||||
| Same | 0.23 | −0.29* | 0.17 | 0.34* | −0.33* | 0.28* |
| (0.17) | (0.12) | (0.11) | (0.15) | (0.13) | (0.12) | |
| Wife higher | −0.06 | −0.62** | −0.16 | 0.18 | −0.53* | 0.16 |
| (0.23) | (0.20) | (0.25) | (0.27) | (0.23) | (0.25) | |
| Urban (yes/no) | 0.33 | 0.53* | ||||
| (0.21) | (0.19) | |||||
| Interactions between couples’ education and urban (wife lower omitted) | ||||||
| Urban*(same education) | −0.54* | −0.73** | ||||
| (0.20) | (0.18) | |||||
| Urban*(wife higher) | −0.57+ | −0.73 | ||||
| (0.30) | (0.43) | |||||
| Paid Work | ||||||
| Couples’ education (wife lower omitted) | ||||||
| Same | −0.02 | 0.56+ | 0.04 | 0.01 | 0.02 | 0.06 |
| (0.20) | (0.30) | (0.20) | (0.14) | (0.10) | (0.11) | |
| Wife higher | 0.27 | 0.60** | 0.34* | −0.40 | −0.30 | −0.30 |
| (0.19) | (0.14) | (0.16) | (0.24) | (0.23) | (0.24) | |
| Urban (yes/no) | −0.10 | 0.15 | ||||
| (0.19) | (0.14) | |||||
| Interactions between couples’ education and urban (wife lower omitted) | ||||||
| Urban*(same education) | 0.55 | 0.03 | ||||
| (0.44) | (0.17) | |||||
| Urban*(wife higher) | 0.26 | 0.00 | ||||
| (0.25) | (0.38) | |||||
| Leisure and Social Activities | ||||||
| Couples’ education (wife lower omitted) | ||||||
| Same | 0.22+ | 0.08 | 0.26 | 0.07 | 0.34+ | 0.09 |
| (0.12) | (0.31) | (0.20) | (0.06) | (0.19) | (0.10) | |
| Wife higher | 0.50* | 0.26 | 0.42* | 0.68** | 0.51* | 0.55** |
| (0.22) | (0.20) | (0.16) | (0.19) | (0.19) | (0.17) | |
| Urban (yes/no) | −0.17 | −0.50* | ||||
| (0.31) | (0.18) | |||||
| Interactions between couples’ education and urban (wife lower omitted) | ||||||
| Urban*(same education) | −0.12 | 0.27 | ||||
| (0.38) | (0.18) | |||||
| Urban*(wife higher) | −0.23 | −0.13 | ||||
| (0.28) | (0.18) | |||||
| Education Activities | ||||||
| Couples’ education (wife lower omitted) | ||||||
| Same | 0.01 | 0.01 | 0.00 | 0.02 | 0.03 | 0.01 |
| (0.01) | (0.01) | (0.01) | (0.01) | (0.04) | (0.01) | |
| Wife higher | 0.07 | 0.06* | 0.05 | 0.01 | 0.06 | 0.01 |
| (0.05) | (0.02) | (0.04) | (0.02) | (0.05) | (0.02) | |
| Urban (yes/no) | 0.01 | 0.00 | ||||
| (0.01) | (0.04) | |||||
| Interactions between couples’ education and urban (wife lower omitted) | ||||||
| Urban*(same education) | −0.00 | 0.02 | ||||
| (0.02) | (0.04) | |||||
| Urban*(wife higher) | −0.01 | 0.05 | ||||
| (0.05) | (0.05) | |||||
| Transportation | ||||||
| Couples’ education (wife lower omitted) | ||||||
| Same | 0.07 | 0.04 | 0.05 | 0.06+ | 0.01 | 0.05 |
| (0.05) | (0.05) | (0.05) | (0.03) | (0.02) | (0.03) | |
| Wife higher | 0.13+ | 0.12** | 0.14+ | 0.15* | 0.07 | 0.16* |
| (0.06) | (0.04) | (0.07) | (0.07) | (0.04) | (0.07) | |
| Urban (yes/no) | 0.06+ | 0.11** | ||||
| (0.03) | (0.04) | |||||
| Interactions between couples’ education and urban (wife lower omitted) | ||||||
| Urban*(same education) | −0.03 | −0.04 | ||||
| (0.04) | (0.04) | |||||
| Urban*(wife higher) | −0.02 | −0.09 | ||||
| (0.05) | (0.07) | |||||
| Other Activities | ||||||
| Couples’ education (wife lower omitted) | ||||||
| Same | −0.22** | −0.26** | −0.21* | −0.37** | 0.01 | −0.38** |
| (0.06) | (0.07) | (0.08) | (0.09) | (0.07) | (0.09) | |
| Wife higher | −0.23* | −0.12 | −0.11 | −0.16 | 0.18 | −0.08 |
| (0.11) | (0.12) | (0.10) | (0.11) | (0.13) | (0.11) | |
| Urban (yes/no) | 0.08 | −0.08 | ||||
| (0.06) | (0.07) | |||||
| Interactions between couples’ education and urban (wife lower omitted) | ||||||
| Urban*(same education) | −0.01 | 0.40** | ||||
| (0.09) | (0.14) | |||||
| Urban*(wife higher) | 0.18 | 0.38+ | ||||
| (0.18) | (0.21) | |||||
| Idle Time | ||||||
| Couples’ education (wife lower omitted) | ||||||
| Same | −0.16 | −0.10 | −0.17 | −0.07 | −0.07 | −0.07 |
| (0.12) | (0.14) | (0.11) | (0.12) | (0.14) | (0.11) | |
| Wife higher | −0.38* | −0.05 | −0.35+ | −0.33* | 0.02 | −0.31* |
| (0.16) | (0.16) | (0.19) | (0.12) | (0.10) | (0.12) | |
| Urban (yes/no) | −0.19 | −0.09 | ||||
| (0.19) | (0.16) | |||||
| Interactions between couples’ education and urban (wife lower omitted) | ||||||
| Urban*(same education) | 0.06 | 0.01 | ||||
| (0.21) | (0.24) | |||||
| Urban*(wife higher) | 0.33 | 0.38* | ||||
| (0.26) | (0.16) | |||||
Notes: Regression coefficients with robust standard errors (clustered at community level) in parentheses are presented in table after adjusting for sampling weights and controlling for the respective spouse education levels, employment status, individual income, and other wife, husband, and household characteristics
p < 0.01
p < 0.05
p < 0.10.
In the major category of personal and household care in urban areas, compared to wives with lower education levels than their husbands, wives with the same or higher education levels than their husbands had smaller gender gaps in time use on both work days (by 0.29 and 0.62 hours, respectively) and non-work days (by 0.33 and 0.53 hours, respectively). The decreases accounted for 11% and 24%, respectively, of the overall average gap in time use in personal and household care on work days (2.58 hours, as presented in Table 2) and 13% and 21%, respectively, on non-work days (an overall gap of 2.55 hours). The analysis of the sub-categories of personal and household care (as presented in Appendix Table 1) showed that most of the reductions in gender gaps were in the time spent on housekeeping and care for family members. In contrast, in the rural areas, wives with the same levels of education as their husbands actually increased the gender gap in time use in personal and household care on non-work days (by 0.28 hours). The sub-category analysis suggested that most of the increase was in the time they spent on housekeeping on non-work days.
These different findings on wives with the same levels of education as their husbands regarding the time use gaps in personal and household care activities were significant across the urban-rural divide on both work days and non-work days, as suggested by the significant interaction terms in the full sample in Table 3. These different patterns could reflect a way for rural wives to compensate for the potential threat to traditional gender roles brought about by their equal levels of education as their husbands, especially in the rural setting. This finding is echoed by the cross-national evidence on the gender deviance neutralization or compensatory gender display perspective, which suggests that women who substantially out-earn their husbands may over-perform housework to compensate or account for traditional gender behaviors (Bittman et al., 2003; Greenstein, 2000; Hochschild, 2012; Hook, 2017). Compared to rural wives with lower education levels than their husbands, those with higher education levels than their husbands, however, were found to have no differences in time use gaps in the major or sub-categories of personal and household care, possibly due to the small size of this group in the rural subsample.
Table 3 shows that the coefficients on paid work for urban wives whose education levels were the same or higher than their husbands were significant and positive on typical work days (0.56 and 0.60 hours, respectively). Similarly, the coefficients for rural wives with higher education levels than their husbands were also significant and positive on typical work days (0.34 hours). Since the differences between wives and husbands in time spent on paid work were negative (as presented in Table 2), these positive coefficients suggest that the gender gaps in time use in paid work were reduced. Almost all this reduction was in the time they spent on full-time work (as shown in Appendix Table 1). The interactions in the analysis of paid work were not significant, which suggests that the associations between couples’ education level difference and their gaps in the time use in paid work were not statistically different across the urban-rural divide.
In the category of leisure and social activities in Table 3, the coefficients on time use gaps were positive among urban wives with the same and higher education levels than their husbands on non-work days (0.34 and 0.51 hours, respectively) and among rural wives with higher levels of education than their husbands on both work and non-work days (0.42 and 0.55 hours, respectively). Similar to the findings on paid work, given the negative differences in time spent on leisure and social activities between wives and husbands (as reported in Table 2), these positive coefficients suggest that gender gaps in time use in these activities were reduced and wives with the same and higher education levels than their husbands increased their time use relative to that of their husbands. These findings suggest an equalizing effect that is also found in many other studies and consistent with the relative resources or bargaining perspective (Bittman et al., 2003; Carlson & Lynch, 2017; Greenstein, 2000). Since the interactions in the full sample were not significant, the findings were not significantly different across the urban-rural divide either.
The analysis of sub-categories of leisure and social activities, as presented in Appendix Table 1, further showed that wives in both urban and rural areas with the same or higher education levels than their husbands increased their time use in reading traditional media on both work and non-work days. The finding was not significantly different across the urban-rural divide. In contrast, while rural wives with the same levels of education as their husbands on both work and non-work days and those with higher levels of education than their husbands on non-work days spent more time on watching TV or listening to radio/music, urban wives with higher levels of education than their husbands spent less time on the same activity on both work and non-work days. These different findings on wives with higher levels of education than their husbands were significant across the urban-rural divide. In addition, urban wives with the same or higher levels of education than their husbands increased their time spent on using the Internet for recreation on both work and non-work days, so did rural wives with higher levels of education than their husband on non-work days. These different patterns were significant across the urban-rural divide. Rural wives with the same levels of education as their husbands spent less time on hobbies, games, and recreational playing.
Table 3 also shows that wives with higher levels of education than their husbands spent more time on work days for those in urban areas and on both work and non-work days for those in rural areas, the finding of which was not significantly different across the urban-rural divide. In contrast, wives with the same levels of education as their husbands spent less time on other activities on work days for those in urban areas and on both work and non-work days for those in rural areas, while the pattern on non-work days was significantly different across urban-rural areas. Lastly, rural wives with higher levels of education than their husbands also spent less time on being idle on both work and non-work days, and the difference on non-work days was significant across the urban-rural divide.
Conclusion and Discussion
Using the 2010 China Family Panel Studies data, this study provides new evidence on the gender gap in time use among Chinese couples in both urban and rural areas, an increasingly important yet understudied topic. We further investigate whether education helps narrow the gender gap in time use among Chinese couples. We find substantial gender gap in time use patterns among Chinese couples. Across urban and rural areas and on both work and non-work days, wives spent much more time on personal and household care, especially on housekeeping and taking care of family members, while husbands spent more time on work and leisure/social activities.
Education, however, played a much more prominent role in narrowing the gender gap in time use for urban than for rural couples. For urban wives, having the same or higher levels of education than their husbands helped narrow the gender gap in time spent on personal and household care, paid work, and leisure/social activities. In addition, higher levels of education of urban wives (in addition to comparing to their husbands’ education levels) helped reduce their time spent on personal and household care and increase their time spent on paid work. For rural wives, however, the role of education in helping narrow the gender gap in time use was mixed. Rural wives with the same or higher levels of education than their husbands were able to increase their time spent on leisure and social activities on both work and non-work days, an equalizing effect that echoes findings in the literature in other countries (Bittman et al., 2003; Carlson & Lynch, 2017; Greenstein, 2000). However, rural wives with the same levels of education as their husbands actually increased their time spent on personal and household care on both work and non-work days, possibly as a way of compensating for the potential threat to traditional gender roles brought about by their equal levels of education as their husbands (Hochschild, 2012; Hook, 2017).
These results provide a nuanced understanding of gender inequality in China as reflected by time use, an important and arguably more accurate measure than many others such as the widely used gender pay gap. This study helps bring China into the growing international literature on gender gaps in both paid and unpaid work and promote the use of time use as a unique perspective. It also highlights the important urban-rural difference in the relationship between education and time use patterns in China, which might have implications for other societies with substantial urban-rural divides. Time use itself, as well as its multidimensional influencing factors in different social and cultural contexts, needs better measurement and more in-depth research across disciplines.
Findings from this study also urge us to consider the mechanisms through which education might reduce the time use gap among married couples. Several channels might be in play. First, more educated wives and husbands could be more likely to shift away from the traditional cognition of gender roles and value gender equity more than their less educated peers. Second, more educated couples, especially those with more educated wives, are more likely to have fewer children and smaller household sizes, possibly leading to a more egalitarian division of household work. Third, it is also possible that more educated couples, especially those with more educated wives, are more likely to seek and be more capable of affording external help. There might be additional effect channels from education to couples’ time use. Future research using quantitative and qualitative data can explore possible effect mechanisms to help us better understand this complicated issue.
As China continues to modernize its labor market and strive to become a world leader, it is important for policymakers to be aware of the magnitude of the persistent gender gap both in the labor market and at the home front. As women in China—and worldwide—become more educated, the unbalanced share of unpaid household work could overburden them and deter their aspirations for education, work, and being fully engaged and contributing members of the society. Meanwhile, a more equal share of paid and unpaid work for men and women would benefit all individuals and families and the society at large. Findings from this study help shed light on these important policy debates in China and around the world.
Acknowledgements
We thank Xiao-yuan Dong, Carl Riskin, Terry Sicular, the editor, and anonymous reviewers for offering constructive feedback and suggestions. This work was supported by the Columbia Population Research Center, which is in turn supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development (P2CHD058486) and the Office of the Provost at Columbia University; and by the Fordham University Research Fellowship at Columbia University. The data used in this project are from the China Family Panel Studies (CFPS) funded by 985 Program of Peking University and carried out by the Institute of Social Science Survey of Peking University. We thank the funders and CFPS team members for providing the data, which makes this study possible.
Appendix Table 1.
Regression results on couples’ education level difference and time use gap in selected sub-categories of activities
| Work Days |
Non-work Days |
|||||
|---|---|---|---|---|---|---|
| Full | Urban | Rural | Full | Urban | Rural | |
| Personal and Household Care | ||||||
| Housekeeping | ||||||
| Couples’ education (wife lower omitted) | ||||||
| Same | 0.02 | −0.10 | 0.03 | 0.15* | −0.14+ | 0.15+ |
| (0.07) | (0.08) | (0.10) | (0.06) | (0.08) | (0.09) | |
| Wife higher | −0.08 | −0.19** | −0.09 | 0.16 | −0.17+ | 0.15 |
| (0.16) | (0.06) | (0.17) | (0.15) | (0.09) | (0.16) | |
| Urban (yes/no) | 0.03 | 0.15 | ||||
| (0.07) | (0.09) | |||||
| Interactions between couples’ education and urban (wife lower omitted) | ||||||
| Urban*(same education) | −0.10 | −0.29* | ||||
| (0.11) | (0.11) | |||||
| Urban*(wife higher) | −0.10 | −0.33+ | ||||
| (0.18) | (0.19) | |||||
| Care for family members | ||||||
| Couples’ education (wife lower omitted) | ||||||
| Same | 0.14 | −0.27* | 0.12 | 0.18 | −0.25 | 0.16 |
| (0.16) | (0.10) | (0.13) | (0.14) | (0.15) | (0.12) | |
| Wife higher | 0.03 | −0.34+ | 0.01 | 0.23 | −0.31+ | 0.23 |
| (0.17) | (0.18) | (0.18) | (0.21) | (0.18) | (0.20) | |
| Urban (yes/no) | 0.36** | 0.41** | ||||
| (0.10) | (0.08) | |||||
| Interactions between couples’ education and urban (wife lower omitted) | ||||||
| Urban*(same education) | −0.41* | −0.43* | ||||
| (0.18) | (0.16) | |||||
| Urban*(wife higher) | −0.35* | −0.52** | ||||
| (0.14) | (0.17) | |||||
| Paid Work | ||||||
| Full-time work | ||||||
| Couples’ education (wife lower omitted) | ||||||
| Same | 0.11 | 0.53+ | 0.20 | 0.06 | −0.05 | 0.14 |
| (0.28) | (0.29) | (0.28) | (0.18) | (0.10) | (0.17) | |
| Wife higher | 0.62+ | 0.65** | 0.61+ | −0.10 | −0.30 | −0.12 |
| (0.35) | (0.13) | (0.32) | (0.52) | (0.24) | (0.45) | |
| Urban (yes/no) | −0.20 | 0.04 | ||||
| (0.16) | (0.13) | |||||
| Interactions between couples’ education and urban (wife lower omitted) | ||||||
| Urban*(same education) | 0.42 | −0.08 | ||||
| (0.48) | (0.21) | |||||
| Urban*(wife higher) | −0.02 | −0.26 | ||||
| (0.30) | (0.70) | |||||
| Leisure and Social Activities | ||||||
| Reading traditional media | ||||||
| Couples’ education (wife lower omitted) | ||||||
| Same | 0.08* | 0.08* | 0.10** | 0.12** | 0.12* | 0.14** |
| (0.03) | (0.04) | (0.02) | (0.03) | (0.05) | (0.03) | |
| Wife higher | 0.19* | 0.31** | 0.21** | 0.27** | 0.32** | 0.27** |
| (0.07) | (0.04) | (0.05) | (0.06) | (0.04) | (0.06) | |
| Urban (yes/no) | −0.08* | −0.09* | ||||
| (0.04) | (0.04) | |||||
| Interactions between couples’ education and urban (wife lower omitted) | ||||||
| Urban*(same education) | 0.01 | 0.01 | ||||
| (0.03) | (0.04) | |||||
| Urban*(wife higher) | 0.12 | 0.04 | ||||
| (0.09) | (0.07) | |||||
| Watching TV or listening to radio/music | ||||||
| Couples’ education (wife lower omitted) | ||||||
| Same | 0.16* | 0.09 | 0.16* | 0.16* | 0.09 | 0.14* |
| (0.07) | (0.14) | (0.07) | (0.07) | (0.12) | (0.06) | |
| Wife higher | 0.18 | −0.21** | 0.18 | 0.35+ | −0.14+ | 0.33+ |
| (0.16) | (0.07) | (0.11) | (0.19) | (0.07) | (0.16) | |
| Urban (yes/no) | 0.03 | 0.02 | ||||
| (0.11) | (0.05) | |||||
| Interactions between couples’ education and urban (wife lower omitted) | ||||||
| Urban*(same education) | −0.06 | −0.06 | ||||
| (0.15) | (0.14) | |||||
| Urban*(wife higher) | −0.36* | −0.44* | ||||
| (0.13) | (0.16) | |||||
| Using Internet for recreation | ||||||
| Couples’ education (wife lower omitted) | ||||||
| Same | 0.01 | 0.11+ | 0.04 | −0.01 | 0.12+ | 0.02 |
| (0.02) | (0.06) | (0.03) | (0.02) | (0.06) | (0.02) | |
| Wife higher | −0.01 | 0.18* | 0.03 | 0.05 | 0.16** | 0.08* |
| (0.03) | (0.06) | (0.03) | (0.04) | (0.04) | (0.03) | |
| Urban (yes/no) | −0.12* | −0.13** | ||||
| (0.06) | (0.04) | |||||
| Interactions between couples’ education and urban (wife lower omitted) | ||||||
| Urban*(same education) | 0.12 | 0.14* | ||||
| (0.07) | (0.06) | |||||
| Urban*(wife higher) | 0.20* | 0.12+ | ||||
| (0.09) | (0.07) | |||||
| Hobbies, games, and recreational playing | ||||||
| Couples’ education (wife lower omitted) | ||||||
| Same | 0.07 | −0.02 | 0.06 | −0.07 | 0.08 | −0.09* |
| (0.06) | (0.06) | (0.07) | (0.04) | (0.06) | (0.04) | |
| Wife higher | 0.09 | −0.01 | 0.01 | 0.00 | 0.10 | −0.07 |
| (0.06) | (0.07) | (0.06) | (0.12) | (0.13) | (0.15) | |
| Urban (yes/no) | −0.07 | −0.16* | ||||
| (0.06) | (0.06) | |||||
| Interactions between couples’ education and urban (wife lower omitted) | ||||||
| Urban*(same education) | −0.11 | 0.13+ | ||||
| (0.11) | (0.07) | |||||
| Urban*(wife higher) | −0.13 | 0.08 | ||||
| (0.10) | (0.21) | |||||
Notes: Regression coefficients with robust standard errors (clustered at community level) in parentheses are presented in table after adjusting for sampling weights and controlling for the respective spouse education levels, employment status, individual income, and other wife, husband, and household characteristics
p < 0.01
p < 0.05
p < 0.10.
Contributor Information
Fuhua Zhai, Associate Professor, Fordham University Graduate School of Social Service, Address: 113 West 60th Street, New York, NY 10023.
Qin Gao, Professor, Columbia University School of Social Work, Address: 1255 Amsterdam Avenue, New York, NY 10027.
Xiaoran Wang, Fordham University Graduate School of Social Service, Address: 113 West 60th Street, New York, NY 10023.
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