Gender inequality of educational resource allocation within - - PDF document

Gender inequality of educational resource allocation within household in China —the effects of resource-limitation and resource-dilution

Yixiao LIU Institute for Population and Development Studies School of Public Policy and Administration Xi’an Jiaotong University Xi’an, Shaanxi Province, 710049, China liuyixiao900522@163.com Quanbao JIANG Institute for Population and Development Studies School of Public Policy and Administration Xi’an Jiaotong University Xi’an, Shaanxi Province, 710049, China recluse_jqb@126.com

Introduction

Gender inequality in education has been viewed as a key indicator of gender

• inequality. In developing countries, girls’ access to schooling has long been a focus of

scholars and policy makers. More than three decades of economic reforms along with the implementation of the one-child policy, the nine-year mandatory education policy have brought great improvements in the quality of life for women and girls in China. Many previous studies show that gender difference in education has been declining in China, and even the educational level of female begin to exceed that of male (Hannum and Xie,1994; Li,2010; Ye and Wu,2011). It has been a consensus among most scholars that the education of female in China is improving and recently more scholars begin to research on the relations between the gender difference in education and other social stratifications.

Some studies revealed that whether parents privilege sons over daughters in educational resource provision is a complicated question. Educational differences between girls and boys have become much more subtle in recent years (Zhang et al., 2007). For example, the study of Li(2009; 2010)shows that gender inequality in education is closely affected by the location of a family, rural or urban, and by the socioeconomic status (SES) of the family. The more remote and the poorer a family is, the greater gender difference in education. Ye and Wu (2011) found that the number of siblings also affects the gender inequality. The more children a family raises, the more likely a female is less advantageous than a male in education. The study of Wu (2012) indicates that the type of gender difference in education varies among social groups. More specifically, the inequality is more severe in families which located in a poorer area, acquire a lower SES and have more children. While in families with higher SES, the gender gap tends to be less and women even have more opportunities in getting education than men. Above all, gender difference in education varies substantially among different social classes. There are three dimensions related to the measurement of educational inequality: educational

• pportunity,

educational process and educational result(Liang, 2012).However, the above studies, no matter on the changes and trends of gender difference in education in China or on the interactions between gender and other social stratifications in education, all focus on individual educational result (educational attainment). There are some specific indicators measuring educational

• result. For example, attending a college or not (Li, 2010), years of schooling (Ye and

Wu, 2011; Wu, 2012), enrollment rates in a certain phase (Connelly and Zheng, 2003)

• r completion or graduation rates (Connelly and Zheng, 2003). Some earlier studies

focused on the educational opportunity, such as illiteracy rates and the rates of dropping-out. However, few of previous studies discussing the relations between the gender inequality and other social dimensions focused on the educational process. The issue of inequality in educational process covers two aspects: one is inequality in school resource provision, and the other is inequality in family resource provision. In contemporary China, the distribution of educational resources is mainly completed in

• families. Liu (2004) has found that basic schooling is totally a private matter and

decisions about it are totally the family’s private business despite of the national Law

• n the 9-year Compulsory Education.

In recent years, researches on gender inequality in education have attracted our attention from overall economic development to individual families (Ye and Wu, 2011). Due to the critical role that family plays in children’s educational process, this study will focus on family educational resource provision. Meanwhile, in the process

• f resource allocation, household will balance efficiency and equity issues to

determine the optimal distribution of educational resources (Behrman, Pollak, and Taubman 1982 ; Becker 1991); Parish and Willis (1993) pointed out that the higher the parents’ income was or the less the siblings are, the smaller the gender gap is. Therefore, when studying gender difference in educational resource allocation within families, considering family resources in this process is necessary. For the family

resources, apart from the household economic conditions, consideration should also be given to the number of children in the families due to resource dilution. And in the context of China, Hukou( household registration dividing Chinese people into urban and rural groups) is a key factor in deciding a family’s welfare. The economic development, the cultural circumstances, the educational infrastructure in rural areas is in a more disadvantaged position than urban areas. Therefore, studies in this field should also consider the type of Hukou of families. Overall, the economic conditions and Hukou represent the situation of resource-limitation of the family and the number

• f children stands for the situation of resource-dilution.

Based on these contexts, the current study uses 2013-2014 baseline survey data of China Education Panel Survey (CEPS) to examine the gender difference of educational resource allocation within household when the family resources are different ( families in different social stratifications).

Literature Review

Research on gender inequality in educational attainment Classic theories for studying resource provision within families come from the human capital model of Becker (1964; 1991) and the resource dilution explanation of Blake (1981). The key point of these two theories is that children’s education is the outcome

• f decision-making of individual families which try to reach a balance between

efficiency and equity while distributing resources. Therefore, both the economic constraints and the dilution of family resources among siblings can have impact on children’s education. Based on this, many scholars enriched the theory in China’s

• context. For example, Parish and Willis (1992) found that the presence of a brother

reduced siblings’ education. Chu, Xie and Yu (2007) developed the theory in China’s special patriarchal society. In empirical studies in Taiwan, they found that the increase

• f the number of siblings had greater impact on girls than on boys and even found that

having a sister who is far older than himself will bring positive influence on a boy’s education. The above classical studies all recruited educational attainment as dependent variable. They used this indicator to reflect the provision and distribution of education resources within families. According to the human capital theories, the number of years staying in school reveals the amount of educational resources one enjoys (Becker, 1964). Moreover, educational attainment is more applicable in studies and as the direct outcome of educating it draws more attention from people. Therefore, in studying the household educational resource provision, it is necessary to review previous studies on educational attainment. Li (2010) found that females from low SES-background families were notably less likely to receive higher education than others. According to the study of Ye and Wu (2011), the more siblings a female had, the less education she was prone to receive, especially when she had brothers. Lee’s study (2011) found that there was no gender difference in education in single-child families, but it still existed in families with

more than one child and in these families girls were in a less advantageous position. The study of Wu and Huang (2015) showed that the difference was greater in rural areas than in urban areas and the socioeconomic index of fathers’ jobs. The education

• f parents and the number of siblings were all closely related with gender difference

in educational attainment. Research on gender inequality in family resource provision in educational process The main forms of family education provision are comprised of emotional support (aspiration and expectation for children’s achievement), financial provision (buying reference books, academic tutorials) and time involvement (supervising and helping children with assignments, talks with children about their schooling). Hannum (2005) adopted family strategies (family choices about investments in sons and daughters) to study gender difference in education. This research focused on household educational expenses on children at 7-16 in China, and found that the education of girls is more prone to be affected by household economic conditions, which was more obvious in rural China. In their analysis of education in rural China, Song, Appleton, and Knight (2006) discovered that among children at the age of 7 to 15, the presence of girls in families did not have a smaller effect on spending than the presence of boys, showing that girls in fact have an advantage in enjoying household investment in education. Tusi and Rich (2002) explored the differences between single-girl and single-boy families with regard to parental expectation and investment in children’s education under the background of China’s one-child policy. In this paper, parental expectations were measured by the number of years that they expect children to stay in school and educational investment by parental talks with child about school, income spent on education, tutorial and praise. In descriptive statistical analysis, the authors found that parents having single-daughters invested more in education than those with single-sons. In addition, female only children were more likely to take academic tutorials than their male counterparts, while male only children were more likely to be praised by their parents for earning good grades. While studying the subject, Li and Tsang (2003) eyed on household decisions in education, namely parental educational expectation and educational spending. The paper selected samples from 4 rural areas in China and discussed gender difference in household decisions in education in rural areas, showing that parents had lower expectations toward girls. The result of a questionnaire survey conducted by Ding (2004), targeting on an impoverished Chinese village of ethnic minorities suggested that all respondents (parents) support (fully or moderately support) the education of sons, but only 76% of fathers and two thirds (about 67%) of mothers support that of

• daughters. The results also indicated that the support girls receive from mothers is

even less than that from fathers for nearly 10%. There is also notable gender difference in household educational expectations toward children: about one third of mothers thought it did not matter whether girls attend school or not. In prospective education levels, mothers had substantially lower expectations on girls than that of

• boys. And the study also found that the primary reason for girls dropping out from

schools was tuition and miscellaneous fees, which in fact indicates that families tend to spend less on girls’ education. Hannum (2009) analyzed the subject from the perspective of the provision of family sources (parental educational attitudes and practices toward boys and girls). Targeting on rural areas of northwestern Gansu province, the study evaluated the provision of family resources, including educational expenses, educational environment (how many books do children have and how frequent parents help with their assignments), the amount of time for study (time for chores indirectly reflecting time for study), mothers’ attitudes toward gender role in education and mothers’ expectations, etc. Empirical analysis of this study suggested that compared with boys, girls faced somewhat lower (though still very high) maternal educational expectations and a greater likelihood of being called on for household chores. However, there was little evidence of a gender gap about economic investments in education. In the part of case study, parents had equally high expectations toward son and daughter. Findings suggested that at least in Gansu, rural parental educational attitudes and practices toward boys and girls were more complicated and less uniformly negative for girls than commonly portrayed. According to previous studies and the current information of the data, this study takes emotional support (parental educational aspiration), financial resources (out-of-school education fee) and time resources (parental educational supervision) as the dependent variables to measure the household educational resource provision. Then we use resource-limitation and resource-dilution as indicators to divide people into different social stratifications. Economic conditions and hukou represent the situation of resource-limitation of the family and number of sibling stands for the status of resource-dilution of the family. The framework of this study is as follows:

Educational resources: emotional support, financial resources, time resources Boys’ education Girls’ education Resource-limitation: Economical conditions; Hukou Resource-dilution: Number of sibling ? ?

Figure1 Framework

Data and Methods

Data This study uses 2013-2014 baseline survey data of China Education Panel Survey (CEPS) conducting by National Survey Research Center at Renmin University of

• China. The respondents are China’s junior high school students of grade seven and

grade nine and their corresponding parents. After excluding some improper

• bservations, the final sample size for the educational emotional support analysis is

17,037, the educational financial resources analysis 16,133 and the educational time resources analysis 14,332. Every record not only contains the information of the student but also information of their corresponding parents. Methods Descriptive statistical methods, Binary Logistic regression model, OLS regression model. Variables Dependant Variables: Emotional support. Here, we focused on parental aspirations for children’s college level education because of the importance of college aspiration for actual college enrollment and, consequently, upward social mobility (Elliott III, 2009; Zhan and Sherraden, 2003). We treated parental aspirations as a dummy variable, with 1 indicating “college education and above” and 0 “less than college education.” Financial resources. The logarithm of spending of extra-class tutorial fee, continuous variable. Time resources. Whether or not supervise their children’s homework, dummy variable with 1 indicating “often or usually” and 0 “scarcely or never”. Focal independent variables: Gender, hukou, number of siblings; family economic conditions. Control variables: Mother’s education, father’s education, parental occupation, children’s ethnicity, parental political identity, grade (seven or nine), regional factor. Children’s academic performance is often considered as an important control in predicting the effects of household economic resources on schooling and parental aspirations (Brown and Park, 2002; Zheng et al., 2002; Deng et al., 2014), so academic performance must be included in the model. Table 1 shows the definition and measurements of all variables in the analysis and table 2 displays the descriptive statistics of focal variables. Table 1 about here Table 2 about here

Results

Emotional support Table 3 shows the results (odds ratio) of binary logistic regression of parental educational aspiration. In model 2, the odds ratio of gender is notably positive, which suggests that in urban families, parents have greater expectation on girls than on boys to receive higher

• education. The interaction term in model 2 indicates that the pattern of gender gap in

rural families is as same as in urban families. But the result is not significant.

The odds ratio of gender in model 3 indicates that in only child families, girls in a more advantaged position in parental education aspiration than boy. However, the interaction tells us that in two-children families the degree of priority of girl to boy declines compared to only girl. The odds ratio of gender in model 4 reveals that gender gap of wealthy families in parental aspiration is significantly big and this advantage towards girls. In ordinary and poor families, this odds ratio for girls declines. The interactions in model 3 and model 4 suggest: having one sibling or being impoverished results in different gender gap in paternal aspiration on the education of children, inflicting negative impact on girls. But no matter in which situations, girls are always in a more advantaged position than boys (see the odds of different combination of gender and social stratifications in table 4)

Table3 about here Table 4 about here

Financial resources Table 5 shows the results of OLS regression of parental educational spending. The exceptionally positive gender coefficient in model 6 shows that among urban families expenses on education for girls are much higher than that for boys. The

• bviously negative interaction coefficient between gender and the type of Hukou

implies that the type of Hukou leads to considerable difference in gender gap in educational expenses. Although rural girls’ advantage declines compared to their urban counterparts, their strengths still exist compared to rural boys (0.52-0.42>0). In model 7, the substantially positive gender coefficient suggests that costs of education for single-daughters are far greater than that of single-sons. However, the interaction coefficient between gender and the number of siblings, pronouncedly negative, indicates that the latter factor has strong effects on gender difference in educational spending. Although influenced by siblings, girls’ situation is still better than boys’ in two-children families (0.53-0.40>0). But in families with more children, girls’ financial resources are less than boys’. The more siblings girls have, the worse their situation is. The gender coefficient in model 8 implies that educational expenses in rich families are much higher than those of boys. The interaction coefficient between gender and household economic conditions is obviously negative, which means that the difference of family wealth has great effect on gender difference in cost of education. The poorer the economic conditions, the more negative the impact on girls. But like results given above, gender inequality in impoverished families is still against boys. To illustrate the relationship between gender and resource on educational spending more clearly, we display figure 2, figure 3 and figure 4. Figure 2 about here

Figure 3 about here Figure 4 about here Time resources Table 6 shows the results of binary logistic regression of parental educational supervision. In model 10 we can see that the odds for girls to enjoy parental supervision are higher than boys in urban families. The interaction tells us that this gender pattern has not changed in rural areas. In model 11 the notably positive odds ratio of gender suggests that parents in single-child families are much more apt to offer girls educational supervision. From the interaction of gender and sibling, it is easy to notice: having one sibling has an

• bvious effect on gender difference in educational supervision and girls’ advantage

declines but is still superior to boys in two-children families. . The gender coefficient in model 12 means that parents in rich families are more inclined to supervise girls than boys. According to the interaction between the two factors, household economic conditions results in great difference in gender gap in educational supervision. Unlike their counterparts, girls in unfavorable economic conditions cannot enjoy usual educational supervision from parents but still

• utbalance boys in the same economic circumstances.

Table 7 displays the different odds in every combination of gender and family resources and the marginal effects bring by resource-limitation and resource-dilution to boys and girls. Table 6 about here Table 7 about here

Conclusions

This study uses 2013-2014 baseline survey data of China Education Panel Survey (CEPS) to examine the gender difference of educational resource allocation within household and the interaction between gender and family resources. The findings of this paper are as follows: This study finds that gender inequality against girls disappears and even the resources supporting girls’ education are more than resources supporting boys. In receiving educational resources, girls are in more advantaged positions and son preference in educational process in Chinese families disappears. This paper also finds that given circumstances with severe scarce resources, son preference still exists in many Chinese families in the process of providing financial resources(see the results of educational spending in families with more than three children and the most impoverished families). Against the backdrop of China’s low birth rate, Chinese families unprecedentedly attach importance to the education of

girls, and one-child policy is thought to have improved the education of Chinese girls (Yang, 2006) and thus bring a higher educational status for Chinese female. Nevertheless, the paper finds that only single-daughters in urban areas or wealthy families are endowed with the “welfare”. Non-single-daughters in poor families cannot benefit from it. This study finds that it is girls who are more prone to be affected by such disadvantages as resource-limitation and resource-dilution and a rural Hukou (see the marginal effects in table 4 and table 7), proving that the resource elasticity of demand for girls’ schooling is higher than that for boys’. All of the above reveal the deep-rooted gender biases against women in China. In single-child families, urban families or wealthy families, girls enjoy advantages in

• education. This, however, indirectly reflects discrimination against women in the job
• market. The daughter from a single-child family in urban areas is the only person

parents can rely on. Given increasingly fierce competition in the job market, the sole resort for such families is to invest more in their daughters and thus help them, as much as possible, to receive more education and acquire better reputation. By doing so, women are likely to win more opportunities in job hunting and only getting a good job can they help their families when their parents get older.

References

Becker, Gary Stanley, and Gary S. Becker. A Treatise on the Family. Harvard university press, 1991. Becker, S. G. . Human Capital: A Theoretical and Empirical Analysis with Special Reference to

• Education. Chicago: The University of Chicago Press, 1964.

Behrman, Jere R., Robert A. Pollak, and Paul Taubman. "Parental preferences and provision for progeny." Journal of Political Economy 90.1 (1982): 52-73. Blake, Judith. "Family size and the quality of children." Demography 18.4 (1981): 421-442. Brown, Philip H., and Albert Park. "Education and poverty in rural China." Economics of education review 21.6 (2002): 523-541. Chu, CY Cyrus, Yu Xie, and Ruoh-rong Yu. "Effects of sibship structure revisited: Evidence from intrafamily resource transfer in Taiwan." Sociology of education 80.2 (2007): 91-113. Chun-ling, L. I. "Gender Differences in Educational Attainment: Impacts of Family Background on Educational Attainment of Men and Women ." Collection of Women's Studies 1 (2009): 004. Chunling, Li. "Expansion of Higher Education and Inequality in Opportunity of Education: A Study on Effect of “Kuozhao” Policy on Equalization of Educational Attainment." Sociological Studies 3 (2010): 82-113. Connelly, Rachel, and Zhenzhen Zheng. "Determinants of school enrollment and completion of 10 to 18 year olds in China." Economics of education review 22.4 (2003): 379-388. Deng, Suo, et al. "Household assets, school enrollment, and parental aspirations for children's education in rural China: Does gender matter?." International Journal of Social Welfare 23.2 (2014): 185-194. Elliott, William. "Children's college aspirations and expectations: The potential role of children's development accounts (CDAs)." Children and Youth Services Review 31.2 (2009): 274-283. Hannum, Emily, and Yu Xie. Trends in educational gender inequality in China: 1949-1985. University

• f Michigan, 1994.

Hannum, Emily, Peggy Kong, and Yuping Zhang. "Family sources of educational gender inequality in rural China: A critical assessment." International journal of educational development 29.5 (2009): 474-486. Hannum, Emily. "Market transition, educational disparities, and family strategies in rural China: New evidence on gender stratification and development." Demography 42.2 (2005): 275-299. Hua, Ye, and Wu Xiaogang. "Fertility Decline and the Trend in Educational Gender Inequality in China." Sociological Studies 5 (2011): 007. Lee, Ming-Hsuan. "The one-child policy and gender equality in education in China: Evidence from household data." Journal of family and economic issues 33.1 (2012): 41-52. Li, Danke, and Mun C. Tsang. "Household decisions and gender inequality in education in rural China." China: An International Journal 1.02 (2003): 224-248. Liu, Fengshu. "Basic education in China’s rural areas: a legal obligation or an individual choice?." International Journal of Educational Development 24.1 (2004): 5-21. Parish, William L., and Robert J. Willis. "Daughters, education, and family budgets Taiwan experiences." Journal of Human Resources (1993): 863-898. Song, Lina, Simon Appleton, and John Knight. "Why do girls in rural China have lower school enrollment?." World Development 34.9 (2006): 1639-1653. Tsui, Ming, and Lynne Rich. "The only child and educational opportunity for girls in urban China." Gender & Society 16.1 (2002): 74-92.

Yang, J. 2006.Has the one-child policy improved adolescents educational wellbeing in China? [Unpublished]. Presented at the Population Association of America 2006 Annual Meeting Los Angeles California March 30-April 1 2006. Yuxiao, Wu. "Gender Gap in Educational Attainment in Urban and Rural China." Society: Chinese Journal of Sociology/Shehui 32.5 (2012). Zhan, Min, and Michael Sherraden. "Assets, expectations, and children’s educational achievement in female-headed households." Social Service Review 77.2 (2003): 191-211. Zheng, Zhenzhen, Ruiqin Niu, and Liqiang Xing. "Determinants of Primary and Middle School Enrollment of 10–18 Year Olds in China." Population and Economics 131.2 (2002): 28-37.

Tables and Figures

Table 1: The definition and measurements of all variables in the analysis Variable Definition and measure of variable Family educational resources Emotional support Parental educational aspiration for children; 1= college education and above, 0 =less than college education Financial resources ln(academic tutorials fees and interest-oriented class this term+1) Time resources Supervise children’s homework; 1=often or usually, 0=never or little Social stratification variables Gender 1=Female, 0=male Hukou 1=Rural, 0=urban Number of siblings Only child 1=Only child, 0=else One sibling 1=One sibling, 0=else Two siblings or above 1=Two siblings or above, 0=else Economic conditions Wealthy 1=Wealthy, 0=else Ordinary 1=Ordinary, 0=else Poor 1=Poor, 0=else Control variables Mother’s education less than junior high school 1=Less than junior high school, 0=else Junior high school 1= Junior high school, 0=else Technical secondary school, vocational high school, senior high school 1= Technical secondary school, vocational high school, senior high school, 0=else Junior college and above 1=Junior college and above, 0=else Father’s education less than junior high school 1= less than junior high school, 0=else Junior high school 1= Junior high school, 0=else Technical secondary school, vocational high school, senior high school 1= Technical secondary school, vocational high school, senior high school, 0=else Junior college and above 1=Junior college and above, 0=else Parental occupation Farmer 1= Farmer, 0=else Administrator, professional and technical 1= Administrator, professional and technical, 0=else Ordinary worker 1=Ordinary worker, 0=else Self-employed 1=Self-employed, 0=else Ethnicity 1= Han ethnicity, 0= ethnic minority Parental political identity 1=Being either a member of the Communist Party or other democratic parties, 0=not a member of any parties Academic performance Parental perception of children’s academic perception Poor 1=Poor, 0=else Medium 1= Medium, 0=else Good 1= Good, 0=else Grade 1= Grade 9, 0=grade 7 Regional factors County, county-level city 1= County, county-level city, 0=else Prefecture-level city 1= Prefecture-level city, 0=else Provincial capital city 1= Provincial capital city, 0=else Province-level municipality 1= Province-level municipality, 0=else

Table 2 Descriptive statistics of all variables in the analyses Variables Emotional support Financial investment Time investment Sample(%) (N=17037) Sample(M/%) (N=16133) Sample(%) (N=14332) Dependent variables Emotional support 86.65 Financial resource 2.36 Time resource 71.48 Focal independent variables Female 49.06 49.20 48.78 Household economy(wealthy) Ordinary 73.38 73.30 74.24 Poor 20.86 20.86 19.68 Rural hukou 55.68 55.69 53.78 Household size(only child) Two children 41.27 41.24 39.74 Three children and above 13.47 13.51 12.34 Control variables Grade 9 47.17 46.82 45.23 Han ethnicity 91.89 91.88 92.52 Academic performance(poor) Medium 42.95 42.83 43.22 Good 33.08 33.43 32.88 Mother’s education(less than junior high school) Junior high school 41.29 41.11 41.17 Technical secondary school, vocational high school, senior high school 21.31 21.34 22.37 Junior college and above 12.97 13.07 14.08 Father’s education(less than junior high school) Junior high school 44.30 44.28 43.26 Technical secondary school, vocational high school, senior high school 25.30 25.30 26.11 Junior college and above 15.30 15.38 16.57 Parental occupation(famer) Administrator, professional and technical 18.11 18.16 19.31 Ordinary worker 29.62 29.50 30.05 Self-employed 20.73 20.58 21.22 Member of the Communist Party or other democratic parties 12.47 12.48 13.09 Regional factors(County, county-level city) Prefecture-level city 17.34 17.19 18.22 Provincial capital city 19.50 19.51 21.27 Province-level municipality 15.30 15.04 16.60 Note: the variables in parentheses in the first column are references.

Table3 Binary logistic regression of parental higher education aspiration, odds ratio (N=17037) Model1 Model 2 Model 3 Model4 Control Variables Mother’s education(Reference: less than junior high school) Junior high school 0.96 0.96

0.96

0.96 (0.06) (0.06)

(0.06)

(0.06) Technical secondary school, vocational high 1.34** 1.34** 1.34** 1.34** school, senior high school (0.12) (0.12) (0.12) (0.12) Junior college and above 2.62*** 2.62*** 2.62*** 2.65*** (0.57) (0.57) (0.57) (0.58) Father’s education(Reference: less than junior high school) Junior high school 1.26*** 1.26*** 1.26*** 1.26*** (0.08) (0.08) (0.08) (0.08) Technical secondary school, vocational high 1.93*** 1.93*** 1.93*** 1.93*** school, senior high school (0.17) (0.17) (0.17) (0.17) Junior college and above 5.41*** 5.41*** 5.40*** 5.38*** (1.12) (1.12) (1.11) (1.11) Parental occupation(Reference: farmer) Administrator, professional and technical 1.87*** 1.87*** 1.87*** 1.87*** (0.23) (0.23) (0.23) (0.23) Ordinary worker 1.27*** 1.27*** 1.27*** 1.27*** (0.08) (0.08) (0.08) (0.08) Self-employed 1.66*** 1.66*** 1.66*** 1.66*** (0.13) (0.13) (0.13) (0.13) Academic performance(Reference: poor) Medium 3.15*** 3.15*** 3.15*** 3.15*** (0.16) (0.16) (0.16) (0.16) Good 14.16*** 14.16*** 14.14*** 14.14*** (1.30) (1.30) (1.29) (1.30) Grade(Reference: seven) 0.69*** 0.69*** 0.69*** 0.69*** (0.03) (0.03) (0.03) (0.03) Regional factor(Reference: county, county-level city) Prefecture-level city 0.72*** 0.72*** 0.72*** 0.72*** (0.05) (0.05) (0.05) (0.05) Provincial capital city 1.17 1.17 1.17* 1.16 (0.09) (0.09) (0.09) (0.09) Province-level municipality 1.02 1.02 1.02 1.02 (0.09) (0.09) (0.09) (0.09) Ethnicity(Reference: ethnic minority) 0.51*** 0.51*** 0.52*** 0.52*** (0.05) (0.05) (0.05) (0.05) Parental political identity(Reference: not a 0.83* 0.83* 0.83* 0.83*

member of any political parties) (0.08) (0.08) (0.08) (0.08) Focal independent variables Gender(Reference: male) 1.30*** 1.30** 1.47*** 1.98** (0.07) (0.11) (0.13) (0.49) Hukou(Reference: urban) 0.89 0.90 0.90 0.89 (0.05) (0.07) (0.05) (0.05) Number of siblings(Reference: zero, only child) One 0.74*** 0.74*** 0.79** 0.74*** (0.05) (0.05) (0.06) (0.05) Two and above 0.58*** 0.58*** 0.61*** 0.58*** (0.05) (0.05) (0.06) (0.05) Family economic conditions(Reference: wealthy) Ordinary 1.40** 1.41** 1.41** 1.62*** (0.17) (0.17) (0.17) (0.24) Poor 1.48** 1.48** 1.48** 1.74*** (0.19) (0.19) (0.19) (0.27) Interaction variables Female×hukou 1.00 (0.11) Female×one sibling 0.82+ (0.09) Female×two and above sibling 0.87 (0.13) Female×ordinary family 0.65+ (0.17) Female×Poor family 0.62+ (0.17)

• 2LL

10840 10840 10836 10836 Wald chi2 1734*** 1735*** 1727*** 1733*** N 17037 17037 17037 17037 Exponentiated coefficients; robust standard errors in parentheses; + p<0.1, *p< 0.05, **p< 0.01, ***p< 0.001; the variables in parentheses in the first column are references. Table4 Higher education aspiration odds in every group and the marginal effect of siblings and poverty Group Odds Marginal effects Male+ only child 65.40*** Male+ one sibling 17.63***

• 47.77***

Male+ two siblings and above 11.07***

• 6.56***

Female+ only child 138.53*** Female + one sibling 26.07***

• 112.45***

Female + two siblings and above 14.80***

• 11.28***

Male+ wealthy household 55.56*** Male+ ordinary household 47.94***

• 7.62

Male+ poor household 14.24***

• 33.70***

Female+ wealthy household 148.08*** Female + ordinary household 76.48***

• 71.59**

Female + poor household 19.93***

• 56.56***

+ p<0.1, *p< 0.05, **p< 0.01, ***p< 0.001

Table5 OLS regression of logged total fees in out-of-school education, coefficients (N=16133) Model5 Model 6 Model 7 Model 8 Control Variables Mother’s education(Reference: less than junior high school) Junior high school 0.11 0.12* 0.11 0.11 (0.06) (0.06) (0.06) (0.06) Technical secondary school, vocational high 0.71*** 0.72*** 0.72*** 0.71*** school, senior high school (0.09) (0.09) (0.09) (0.09) Junior college and above 1.26*** 1.27*** 1.26*** 1.27*** (0.14) (0.14) (0.14) (0.14) Father’s education(Reference: less than junior high school) Junior high school 0.04 0.04 0.04 0.04 (0.06) (0.06) (0.06) (0.06) Technical secondary school, vocational high 0.36*** 0.36*** 0.36*** 0.36*** school, senior high school (0.09) (0.09) (0.09) (0.09) Junior college and above 0.66*** 0.65*** 0.65*** 0.65*** (0.13) (0.13) (0.13) (0.13) Parental occupation(Reference: farmer) Administrator, professional and technical 0.75*** 0.74*** 0.75*** 0.74*** (0.11) (0.11) (0.11) (0.11) Ordinary worker 0.21*** 0.20*** 0.21*** 0.21*** (0.06) (0.06) (0.06) (0.06) Self-employed 0.58*** 0.58*** 0.59*** 0.58*** (0.07) (0.07) (0.07) (0.07) Academic performance(Reference: poor) Medium 0.29*** 0.29*** 0.29*** 0.29*** (0.06) (0.06) (0.06) (0.06) Good 0.21** 0.21** 0.21** 0.21** (0.07) (0.07) (0.07) (0.07) Grade(Reference: seven) 0.56*** 0.56*** 0.57*** 0.56*** (0.05) (0.05) (0.05) (0.05) Regional factor(Reference: county,

county-level city) Prefecture-level city 1.08*** 1.09*** 1.08*** 1.08*** (0.08) (0.08) (0.08) (0.08) Provincial capital city 2.36*** 2.37*** 2.37*** 2.36*** (0.08) (0.08) (0.08) (0.08) Province-level municipality 1.72*** 1.73*** 1.72*** 1.72*** (0.09) (0.09) (0.09) (0.09) Ethnicity(Reference: ethnic minority)

• 0.07
• 0.07
• 0.07
• 0.07

(0.08) (0.08) (0.08) (0.08) Parental political identity(Reference: not a 0.29*** 0.29*** 0.28*** 0.29*** member of any political parties) (0.09) (0.09) (0.09) (0.09) Focal independent variables Gender(Reference: male) 0.29*** 0.52*** 0.53*** 0.91*** (0.05) (0.08) (0.08) (0.25) Hukou(Reference: urban)

• 0.63***
• 0.43***
• 0.62***
• 0.63***

(0.06) (0.08) (0.06) (0.06) Number of siblings(Reference: zero, only child) One

• 0.29***
• 0.28***
• 0.11
• 0.29***

(0.06) (0.06) (0.08) (0.06) Two and above

• 0.40***
• 0.38***
• 0.12
• 0.40***

(0.08) (0.08) (0.10) (0.08) Family economic conditions(Reference: wealthy) Ordinary

• 0.43***
• 0.42***
• 0.42***
• 0.16

(0.13) (0.13) (0.13) (0.17) Poor

• 0.69***
• 0.68***
• 0.68***
• 0.27

(0.14) (0.14) (0.14) (0.18) Interaction variables Female×hukou

• 0.42***

(0.10) Female×one sibling

• 0.40***

(0.11) Female×two and above sibling

• 0.55***

(0.13) Female×ordinary family

• 0.59*

(0.25) Female×Poor family

• 0.91***

(0.26) Constant 0.99*** 0.85*** 0.86*** 0.70*** (0.18) (0.18) (0.18) (0.20) Adj-R2 0.267 0.268 0.268 0.268 F 265.27*** 255.38*** 245.14*** 244.97*** N 16133 16133 16133 16133

Coefficients; robust standard errors in parentheses; + p<0.1, *p< 0.05, **p< 0.01, ***p< 0.001; the variables in parentheses in the first column are references. Table6 Binary logistic regression of parental supervision on education, odds ratio (N=14332) Model 9 Model 10 Model 11 Model 12

Control Variables Mother’s education(Reference: less than junior high school) Junior high school 1.43*** 1.43*** 1.43*** 1.42*** (0.07) (0.07) (0.07) (0.07) Technical secondary school, vocational high 1.74*** 1.74*** 1.74*** 1.74*** school, senior high school (0.12) (0.12) (0.12) (0.12) Junior college and above 1.90*** 1.90*** 1.90*** 1.91*** (0.20) (0.20) (0.20) (0.20) Father’s education(Reference: less than junior high school) Junior high school 1.25*** 1.25*** 1.25*** 1.25*** (0.07) (0.07) (0.07) (0.07) Technical secondary school, vocational high 1.31*** 1.31*** 1.31*** 1.30*** school, senior high school (0.09) (0.09) (0.09) (0.09) Junior college and above 1.55*** 1.55*** 1.55*** 1.55*** (0.16) (0.16) (0.16) (0.16) Parental occupation(Reference: farmer) Administrator, professional and technical 1.23* 1.23* 1.23* 1.23* (0.10) (0.10) (0.10) (0.10) Ordinary worker 0.99 0.99 0.99 0.99 (0.06) (0.06) (0.06) (0.06) Self-employed 1.09 1.09 1.09 1.08 (0.07) (0.07) (0.07) (0.07) Academic performance(Reference: poor) Medium 1.80*** 1.80*** 1.80*** 1.80*** (0.09) (0.09) (0.09) (0.09) Good 2.63*** 2.63*** 2.63*** 2.63*** (0.14) (0.14) (0.14) (0.14) Grade(Reference: seven) 0.61*** 0.61*** 0.61*** 0.61*** (0.02) (0.02) (0.02) (0.02) Regional factor(Reference: county, county-level city) Prefecture-level city 1.30*** 1.31*** 1.31*** 1.30*** (0.08) (0.08) (0.08) (0.08) Provincial capital city 1.00 1.00 1.00 1.00 (0.06) (0.06) (0.06) (0.06) Province-level municipality 1.67*** 1.67*** 1.67*** 1.66*** (0.12) (0.12) (0.12) (0.12) Ethnicity(Reference: ethnic minority) 1.48*** 1.48*** 1.48*** 1.48***

(0.11) (0.11) (0.11) (0.11) Parental political identity(Reference: not a 1.04 1.04 1.04 1.04 member of any political parties) (0.07) (0.07) (0.07) (0.07) Focal independent variables Gender(Reference: male) 1.46*** 1.53*** 1.64*** 1.96*** (0.06) (0.10) (0.10) (0.34) Hukou(Reference: urban) 0.93 0.96 0.93 0.93 (0.04) (0.06) (0.04) (0.04) Number of siblings(Reference: zero, only child) One 0.78*** 0.78*** 0.85** 0.78*** (0.04) (0.04) (0.05) (0.04) Two and above 0.61*** 0.62*** 0.63*** 0.61*** (0.04) (0.04) (0.06) (0.04) Family economic conditions(Reference: wealthy) Ordinary 1.24* 1.24* 1.24* 1.38** (0.11) (0.11) (0.11) (0.15) Poor 1.10 1.10 1.11 1.29* (0.11) (0.11) (0.11) (0.16) Interaction variables Female×hukou 0.92 (0.07) Female×one sibling 0.80* (0.07) Female×two and above sibling 0.92 (0.11) Female×ordinary family 0.75 (0.13) Female×Poor family 0.68* (0.13)

• 2LL

15518 15516 15510 15512 Wald chi2 1432*** 1428*** 1426*** 1434*** N 14332 14332 14332 14332 Exponentiated coefficients; robust standard errors in parentheses; + p<0.1, *p< 0.05, **p< 0.01, ***p< 0.001; the variables in parentheses in the first column are references. Table7 Education supervision odds in every group and the marginal effect of siblings and poverty Group Odds Marginal effects Male+ only child 3.88 *** Male+ one sibling 2.20 ***

• 1.68 ***

Male+ two siblings and above 1.43 ***

• 7.66***

Female+ only child 7.44 *** Female + one sibling 3.17 ***

• 4.27 ***

Female + two siblings and above 2.15 ***

• 1.02***

Male+ wealthy household 3.03*** Male+ ordinary household 3.32*** 0.30 Male+ poor household 1.87***

• 1.45***

Female+ wealthy household 7.07*** Female + ordinary household 5.27***

• 1.80+

Female + poor household 2.48***

• 2.80***

+ p<0.1, *p< 0.05, **p< 0.01, ***p< 0.001

Figure 3 Fees in out-of-school education by gender and hukou Figure 4 Fees in out-of-school education by gender and number of siblings

1 2 3 4 ln(fees in out-of-school education+1) Urban Rural Male Female 1 2 3 4 ln(fees in out-of-school education+1) 0 sibling 1 sibling 2+siblings Male Female

Figure 5 Fees in out-of-school education by gender and household economic conditions

1 2 3 4 5 ln(fees in out-of-school education+1) Wealthy Ordinary Poor Male Female