A Study of Rural Chinese Students Educational Expenses, Academic Performance, and Extracurricular Activities

Transcription

1 JOURNAL OF CHINESE ECONOMICS, 2016 Vol. 4. No. 1, pp (Online Version) A Study of Rural Chinese Students Educational Expenses, Academic Performance, and Extracurricular Activities Shishu Zhang 1 and Greg J. Soukup 2 Abstract: Increases in tuition and educational expenses make it increasingly difficult for rural families to send their children to school (Sun, 2000). Many families feel education is useless in rural China (Shek, Tsoi et al., 2001) and send their children to work in big cities to ease their economic burdens (Tabachnick & Fidell, 2001). The researchers examined challenges students from rural China face in obtaining a higher education. The academic performance and extracurricular activities of students in rural China were analyzed. Data from the Chinese Household Income Project (CHIP 2002, CHIP 2007) were analyzed. T-test analyses of the 2002 survey (7,241 observations) determined significant differences in educational expenses and extracurricular activities for students from different grade levels. There were significant differences in yearly educational expenses by grade level with college students having the highest educational expenses (RMB 6,845) and primary students the lowest expenses (RMB 427). Multiregression and multilogit analyses were used to examine possible significant differences between school years completed by the student (dependent variable) and student extracurricular activities (independent variable). Other control variables examined included age, parental expectations, family expenses and afterschool work activities. The multiregression analysis results show that higher parental expectations, increased time spent after school engaged in physical chores, and less time watching television increased rates of educational attainment. Multiple regression analysis was used on the CHIP 2007 data (342 observations) to determine significant factors that impacted school enrollment in rural China. Student population was the dependent variable and the number of teachers per village was the independent variable. Other control variables analyzed included teacher monthly salaries, travel time to school, physical condition of schools, school funding and types of transportation used by students to attend school. The multiregression analysis of the CHIP 2007 survey found more teachers living in the village where they taught and shorter travel times to school were associated with more students enrolled in schools. The authors recommend that the Chinese government provide funds to attract more faculty and administrators back to the countryside and provide more educational funding and scholarships directly to rural families. Key words: Educational Expenses, Academic Performance, Extracurricular Activities, Rural Education in China, Rural Students in China. JEL Classification: A20 I25 I28 O53 1 Associate Professor of Economics, H-E-B School of Business, University of the Incarnate Word, San Antonio, Texas, szhang@uiwtx.edu. 2 Professor of Kinesiology, School of Nursing and Health Profession, University of the Incarnate Word. San Antonio, Texas, soukup@uiwtx.edu.

2 14 Zhang and Soukup 1. Introduction Education is a major expense for many families in rural China. Research has shown that a very significant percentage of family income is devoted towards the education of children. Parents devote 46% of family income towards secondary school expenses, 51% on high school expenses and 52% on university expenses for their children (Dong & Wan, 2012). Educational expenses since 2000 have dramatically increased and made it very difficult for Chinese families to afford higher education for their children. Unauthorized charges by Chinese schools have amounted to RMB 200 billion (US $32 billion) in the last decade. Students must now pay RMB 10,000 (US $1,600) in various university fees (compared to only RMB 200 (US $32) in the late 1980s (Dong & Wang, 2012)). Researchers in 2003 found it cost around RMB 250,000 (US $40,270) to raise a child from birth until the age of 16. If a child went to a university the total cost was driven up by RMB 140,000 (US $22,500) (Dong & Wan, 2012). After graduation from college, parents also spent another RMB 100,000 (US$16,100) on helping their children afford housing and wedding expenditures. Overall, the average Chinese parent will spend 40 to 50% of their total income raising their child. Why have educational expenses risen so dramatically in the last decade? The commercialization of education by schools has resulted in significant increases in tuitions and fees. Even public schools now charge students several hundred RMB Yuan (US $50) to attend elementary and middle schools classes. Dong& Wan (2012) found that 20% of students in 40 schools from 10 cities paid sponsorship and school selection fees for classes. Salaries for university administrators and expenses for higher education have dramatically increased the expense of attending universities during the last decade. Educational funding and assistance for students by the government has not kept up with the ever increasing cost of education. The academic burden of Chinese students is one of the highest in the world. This study will examine the impact that various economic factors have on the educational attainment of students from rural China and the researchers will also determine how factors like faculty size, school infrastructure and travel distance impact student enrollment in schools. 2. Review of Literature 2.1 Educational Expenses in Rural China Recent increases in health and educational expenses in China and reductions in public funding have increased financial pressures on Chinese families living in rural areas. Tsang (1996) and Hannum (1998) reported that many schools have increased fees to offset rising costs resulting from educational decentralization in China. Hossain (1996) reported that the poorest quintile of households in China spent 14.2% of their annual income on education and the wealthiest quintile spent only 5.5%. Several researches have documented that health and educational expenses forced many people into poverty in lesser developed western regions (Bloom and Tang, 1999; Bloom and Gu, 1997, Beach, 2001; Bray and Borevskaya, 2001; Mok, 2000). Gustafsson and Li (2003) investigated how health and educational expenses impacted families in 18 provinces from 1988 to 1995 and found that poverty in China was the most concentrated in the west. Educational expenses created heavy burdens on families in rural China and many were starting to question the value of the investment. Educational levels were also found to differ significantly between the sexes with men from rural areas obtaining an average of 6.5 years of education and women

3 A Study of Rural Chinese Students Education 15 about 4 years (Zhang, Huang & Rozelle, 2003). The educational attainment of Chinese from rural areas was significantly less than individuals from urban areas (China Statistical Yearbook, 2000). Higher educational costs and difficulties in finding jobs have created economic hardships for many students and their families from rural areas (Shek, 2002; Wang & Xie, 2006). From the 1970s to early 1990s approximately 30% of students that attended top-tier Chinese universities were from rural areas. Wang & Xie (2006) reported that the proportion of students from China's rural areas attending key universities had fallen significantly since the 1990s. In 2012, the toptier universities in China reported that about 10% of their students were from rural areas. Large numbers of students and their families believe that, education is useless in rural China (Shek, Tsoi et al., 2001) and many are sending their children to work in big cities to help ease the economic burdens on their families (Tabachnick & Fidell, 2001). 2.2 Other Educational Challenges in Rural China Hannum (1998) used census data to determine that boys were more likely to enroll in school than girls and that this gap further increased among poorer families. Poorly educated parents were less likely to emphasize and provide higher education for their children. Lack of community resources in poor and undeveloped areas often led to lesser performing schools and lower enrollment rates. The benefits of advanced education in many remote undeveloped areas for rural families are so insignificant that they are discouraged from investing in higher education for their children. After controlling for wealth and expenditure, the researchers found that educated parents in China were more likely to educate their children (Jamison and Van der Gaag, 1987; Connelly and Zheng, 2003). Each additional year of a father s educational attainment reduced the likelihood of a student dropping out of school by 12 14% and also decreased the possibility of a child being held back a grade in school by 14%. Better performing schools had significantly higher enrollment rates regardless of student academic ability or family income (Hanushek, Lavy & Hitomi, 2008). Increases in educational expenses have been shown to significantly reduce the rates of female student enrollment in developing countries (World Bank, 2000). Brown and Park (2001) found that children from poorer households were three times as likely to drop out of school and dropped out of schools significantly earlier than children from wealthier households. 2.3 Educational Rewards in Rural China Since the 1980s, Chinese markets have been dependent on the labor of large numbers of workers from rural areas to facilitate a shift from a mainly agricultural to industrial economy. With the development of rural areas, the percentage of rural laborers that have entered urban work forces rose from 22% in 1988 to 34% in 1995 (Rozelle et al., 1999). By 2000, nearly 200 million Chinese agricultural workers had taken up non-agricultural and industrial jobs. By the end of the 1980s, wages for workers with higher education were actually below those of individuals with elementary school educations. However, by the mid-1990s incomes for individuals with middle school education and above on average had risen significantly higher than the earnings of workers with only an elementary education (Hannum, 2003). Zhang, Huang and Rozelle (2002) found that between 1988 and 1992 the income of rural workers with middle school or higher education levels were approximately 50% higher than workers with less education and by 1996 differences in earnings had risen to more than 100% between the groups.

4 16 Zhang and Soukup Research regarding the impact that education has had on rural Chinese workers has been conflicting. Benjamin and Brandt (1997), Liu, Carter, and Yao (1998), Meng (1990) and Meng (1995) concluded that higher educational attainment did not generally result in better jobs or wages for rural Chinese workers. Other researchers have found that these labor markets have emerged in a positive way due to increases in educational attainment. Maurer-Fazio (1999) and Zhang, Huang and Rozelle (2002) determined that wages increased significantly for rural workers because of improvements in education. Researchers have concluded that individuals with higher rates of education have benefited more than people with less education. More educated individuals have access to more higher paying jobs than lesser educated individuals that stay in rural western China. Chinese workers that obtain higher education could use their expertise and financial assets to help further enrich themselves and to develop agrarian economies in western China. 3. Purpose of Study Changes in economics, attitudes and family structure in rural China have created many new challenges for education. Increases in tuition and educational expenses make it increasingly difficult for families to send their children to school (Sun, 2000). Many families feel education is useless in rural China (Shek, Tsoi et al., 2001) and are sending their children to work in big cities to help ease their economic burdens (Tabachnick & Fidell, 2001). Growing numbers of parents in rural China are leaving their children with family members to work in large cities and these children account for 57.14% of children with psychological problems and 70% of juvenile delinquents in China (Liu, 2014). This study examined data from the CHIP 2002 and 2007 surveys to update research on the educational situation faced by families in rural China. The researchers examined how increased educational expenses, school transportation, after school activities and parental expectations affected the educational attainment of students and how factors like student teacher ratios and school infrastructure influenced school enrollment. Analysis also determined rates of students attending primary, middle, senior and technical schools and colleges from rural Chinese communities. 4. Data Data from the Chinese Household Income Project (Shi, 2002; CHIP, 2007) were analyzed to describe the condition of rural education in China. The 8 th dataset of the CHIP 2002 survey contained 7241 observations that were analyzed to study the impact that family income, family educational expenditures and after-school activities had on the educational achievement of students in rural China. A descriptive analysis was executed to determine the most time consuming after school activities for students.

5 A Study of Rural Chinese Students Education 17 Table 1 Description of both Continuous and Categorical Variables (2007) VARIABLE Freq Percent Whether the village has primary school or not 1. The village has complete primary school % 2. The village has other kind of primary school % 3. The village has education facility % 4. The village has no education facility % Whether the schools in the village have the funding problem? 1. very difficult % 2. relatively difficult % 3. not difficult % Whether the schools in the village have endangered houses? 1. The village has dangerous house % 2. The village doesn t have dangerous house % What is the best way to get to primary school? 1. Walk % 2. Bike % 3. Tractor % 4. Animal drawn vehicle % 5. Motorcycle % 6. Car % 7. Others % How long to get to primary school (hours)? 1. Within 15 minutes % 2. From 15 to 30 minutes % 3. From 30 to 60 minutes % 4. From 60 to 90 minutes % 5. Above 90 minutes % Does the village have middle school or not? 1. The village has middle school % 2. The village doesn t have middle school % Best way to get to nearest middle school 1. Walk % 2. Bike % 3. Tractor % 4. Animal drawn vehicle % 5. Motorcycle 0 0% 6. Car % 7. Others % TIME: Time to get to nearest middle school (hours)? 1. Within 15 minutes % 2. From 15 to 30 minutes % 3. From 30 to 60 minutes % 4. From 60 to 90 minutes % 5. Above 90 minutes %

6 18 Zhang and Soukup Table 1(Continue) Description of both Continuous and Categorical Variables (2007) VARIABLE Mean SD How many teachers in a village? How many part-time teachers in a village? How many students in each school? What is teacher s monthly salary? 1, What is total stipend for each teacher in the village? , Table 2 Descriptions of the Continuous Variables (2002) Definitions Obs. ME SD Age 8, Has the child finished 9 years compulsory education 8, If the child is schooling now, the school is 7, How many schooling years has the child finished? 7, You hope the child will 7, The total educational expenses for the child in 2002 (Yuan) 7,581 1, , Educational expenses: tuition 7, , Educational expenses: fee for books 7, Educational expenses: fee for school uniform 4, Educational expenses: other educational expenses 6, Child always does after school: homework 7, Child always does after school: agriculture activities 7, Child always does after school: non-agriculture activities 7, Child always does after school: non-agriculture activities 7, Child always does after school: housework 7, Child always does after school: take care of other child in the family 7, Child always does after school: play 7, Descriptive Statistics for CHIP 2007 Table 1 presents results of the data analysis regarding teacher salaries, funding, housing and transportation for primary and middle school students in rural China. On average, there were 17 teachers for 350 students in each school. Annual teacher salaries averaged RMB with an additional government stipend that averaged RMB for each teacher in the village. Of the schools surveyed 304 had complete primary school facilities and 332 villages had none. Data regarding funding problems determined that 85 (17.31%) schools stated they had very difficult problems finding funding, 263 (53.56%) schools reported relatively difficult funding problems and 143 (29.12%) schools reported no funding problems. The report also found that 15.75% of village housing was dangerous. Several different types of school transportation were reported for primary students with 58.37% of students walking, 29.47% riding bikes,.29% using tractors,.14% animal drawn vehicles, 3.86% motorcycles and 7.44% using automobiles. The majority of primary school students 318 (45.43%) reported less than a 15 minute travel time to school, 276 (39.43%) took 15 to 30 minutes, 88 (12.57%) 30 to 60 minutes, 13 (1.86%) 60 to 90 minutes and 5 (.71%) reported travels times of over 90 minutes.

7 A Study of Rural Chinese Students Education 19 Only 82 (10.25%) of villages had middle schools. The majority of middle school students 55.89% rode bicycles to school, 22.69% walked, 4.56% used animal drawn vehicles, 4.01% automobiles and.76% tractors. Reported transportation times to middle school were 27.16% less than 15 minutes, 45.56% were 15 to 30 minutes, 22.65% were 30 to 60 minutes, and 0.63% were over 90 minutes. Table 3 Description of Categorical Variables (2002) Categorical Variables Freq. Percent Child s schooling 1 primary school 3, % 2 technical secondary school (vocational senior middle school) % 3 junior middle school in the village/in other village 2, % 4 senior middle school in the county/township or in other % county/township 5 college and university % Expectation of the child 1 graduate from primary school % 2 graduate from junior middle school % 3 graduate from technical secondary school % 4 graduate from senior middle school % 5 graduate from the college or university 5, % 6 graduate student % Whether do homework or not 0 No % 1 Yes 6, % Whether to do agriculture work or not 0 No 5, % 1 Yes 1, % Whether to do non-agriculture or not 0 No 7, % 1 Yes % Whether to do housework or not 0 No 4, % 1 Yes 2, % Whether to take care of children or not 0 No 6, % 1 Yes % Whether to play or not 0 No 2, % 1 Yes 5, % What the children do after school 1 agriculture activities 1, % 2 non-agriculture activities % 3 non-agriculture private business % 4 housework 1, % 5 take care of other child in the family % 6 watch TV 3, % 7 other 1, %

8 20 Zhang and Soukup 4.2 Descriptive Continuous Variable Statistics for the CHIP 2002 Table 2 provides results from the CHIP 2002 study related to years of student educational attainment, educational expenses and after-school activities for children in rural China. There were 8,121 observations in total and the average age for children in the sample was 13 years-old with 48.83% in primary school, 3.79% in technical secondary school, 35.25% in junior middle school, 9.34% senior middle school and 2.79% attended university. The majority of parents (69.18%) expected their children to graduate from college or university and 10.4% expected their children to attend graduate school. On average, students finished 6.16 years of school. Educational expenses averaged RMB 1, per year per student of which RMB were spent on tuition, RMB on school uniform, and RMB spent on other related educational expenses. 4.3 Descriptive Categorical Variable Statistic for the CHIP 2002 Table 3 describes the categorical variables for the CHIP 2002 Survey. The majority of the students 3,722 (48.83%) were primary school students, 2,687 (35.25%) junior middle school students, 712 (9.34%) of the sample were senior middle school students, 289 (3.79%) technical school or vocational senior middle school students, and 712 (2.79%) of the sample were college or university students. Only 20 (.26%) of the parents expected a minimal educational attainment of primary school for their children, 481 (6.33%) expected their children to graduate from junior middle school, 314 (4.14%) expected the children to graduate from technical secondary school, 735 (9.68%) expected their children to graduate from senior middle school, 5,253 (69.18%) expected college or university graduation and 790 (10.40%) of the parents thought their children would complete graduate school. After school activities for students were that 91.45% did homework, 23.48% agriculture work, 1.26% non-agricultural work, 35.62% housework, 7.38% child care, 70.55% played and 47.43% watched TV. 5. Methodology T-test, multiple regression and multilogit analyses were used to analyze data from the CHIP 2002 survey (7,241 observations). T-tests were used to determine if significant differences related to tuition and educational expenses and time spent on various after school activities were significantly different by grade level. Multiregression and a multilogit analyses were used to examine possible significant differences between school years completed by the student (dependent variable) and student extracurricular activities (independent variable). Other control variables examined included age, parental expectations, family expenses and after-school work activities. Multiple regression analysis was used on the CHIP 2007 data (342 observations) to determine significant factors that impacted school enrollment in rural China. Student population was the dependent variable and the number of teachers per village was the independent variable. Other control variables analyzed included teacher monthly salaries, travel time to school, physical condition of schools, school funding and types of transportation used by students to attend school.

9 A Study of Rural Chinese Students Education Results 6.1 Tuition and Total Educational Expenses Table 4 describes tuition and total educational expenses for the different groups. Parents of primary school students paid RMB 427 per year compared to RMB 890 for junior school students and the differences were statistically significant. Tuition expenses for primary school were RMB 205per student and RMB 408 for junior school students and the two groups significantly differed from each other. Fees for primary school are RMB 108 and RMB 190 for junior school and again the two groups were significantly different. Total expenses for junior students were RMB and RMB 2, for senior students with differences between the two groups being statistically significant. Tuition for junior students was RMB 408 compared to RMB 1,243 for senior school students. Fees for the junior students were RMB 190 and RMB 345 for senior students. T-test analyses showed significant statistical differences related to expenses between junior and senior students in all categories. Total expenses for senior students were RMB 2,547 and RMB 6,845 for college students. Tuition costs for senior students were RMB 1,243 and RMB 4,107 for college students. Fees were RMB 354 per year for senior students and RMB 539 for the college students. T-tests showed all three expense categories between senior and college students were significantly different. Primary school student total expenses were RMB 427 per year, RMB 205 for tuition and RMB 108 for fees compared to senior school students whose parents paid RMB 2,547 in total expenses, RMB 1,243 for tuition, and RMB 354 for fees. T-test analyses determined statistically significant differences in all three categories between primary and senior school students. Total expenses for senior students were RMB 2,547 per year, RMB 2,129 for tuition, and RMB 347 for fees while the parents of technical school students paid RMB 3,502 in total expenses, RMB 1,243 for tuition and RMB 190 for fees. T-tests determined significant differences between senior and technical school students on total expenses, tuition and fees related to school. In conclusion, college students had the highest total expenses of RMB 6,845 per year, technical student expenses were RMB 3,502, senior student expenses were RMB 2,547, junior school expenses were RMB 890 and primary school expenses were the least costly at RMB 427 per year Time Spent on Different After School Activities Table 5 illustrates the results of different activities that students participated in after school. The survey reported that among primary students 94% did homework, 18% agricultural work, 1% nonagricultural work, 31% housework and 82% played after-school. Results determined 93% of junior school students did homework, 30% did agricultural work, 1% did nonagricultural work, 44% did housework, and 66% played after school. Eighty-eight percent of senior school students did homework, 27% did agricultural work, 2% did nonagricultural work, 36% did housework, and 46% played after-school. College students reported that 69% did homework, 16% did agricultural work, 2% did nonagricultural work, 18% did housework, and 38% played afterschool. The technical school students reported that 74% did homework, 20% did agricultural work, 2% did nonagricultural work, 31% did housework, and 46% played after-school.

10 22 Zhang and Soukup Table 4 Tuition Expenses and Total Educational Expenses of Different Groups (2002) Compare Group Primary Junior t-value Total Expenses ±5.75 (3683) ±19.25(2677) (6358) Tuition ±3.48(3392) ±13.20(2503) (5893) Fees ±1.61(3491) ±3.17(2520) (6009) Compare Group Junior Senior t-value Total Expenses ±19.25(2677) 2,547.48±87.30(706) (3381) Tuition ±13.20(2530) 1,243.17±54.07(699) (3200) Fees ±3.17(2520) ±10.59(674) (3192) Compare Group Senior College t-value Total Expenses 2,547.48±87.30(706) 6,845.82±266.62(209) (913) Tuition 1,243.17±54.07(699) 4,107.40±195.29(203) (900) Fees ± ± (864) Compare Group Primary Senior t-value Total Expenses ±5.75 (3683) 2,547.48±87.30(706) (4387) Tuition ±3.48(3392) 1,243.17±54.07(699) (4089) Fees ±1.61(3491) ±10.59(674) (4163) Compare Group Senior Technic t-value Total Expenses 2,547.48±87.30(706) 3,502.57±143.49(286) 5.80(990) Tuition 2,129.62±101.55(284) 1,243.17±54.07(699) 8.31(981) Fees ±22.64(266) ±3.17(2520) 12.88(2784) *in the bracket is number of observations and degree of freedom Primary school students spent significantly less time doing agricultural chores and housework and more time engaged in play after school than junior school students. The primary school students also had significantly higher rates of after school homework and play time than college students but college students had higher rates of agricultural work and housework. Junior school students had significantly higher rates of housework and play time after school than senior school students. Senior school students had significantly higher rates of after school homework, agricultural work, housework and play than college students. Senior school students also had significantly greater rates of after school homework than technical school students Factors That Affect Student Population A regression model was used to estimate student population determinants. Y is a continuous variable indicating student populations in different villages. (1)

11 A Study of Rural Chinese Students Education 23 Table 5 Time Spent on Different Activities (2002) Compare Group Primary Junior t-value p-value Homework 0.94(3654) 0.93(2642) 1.09(6294) 0.28 Agriculture 0.18(3674) 0.30(2650) (6322) 0.00 Non-agriculture 0.01(3652) 0.01(2628) -2.25(6278) 0.02 Housework 0.31(3668) 0.44(2646) (6312) 0.00 Play 0.82(3683) 0.66(2641) 15.12(6322) 0.00 Compare Group Junior Senior t-value p-value Homework 0.93(2642) 0.88(697) 4.21(3337) 0.00 Agriculture 0.30(2650) 0.27(705) 1.98(3353) 0.05 Non-agriculture 0.01(2628) 0.02(699) -0.45(3325) 0.66 Housework 0.44(2646) 0.36(706) 3.90(3350) 0.00 Play 0.66(2641) 0.46(701) 9.57(3340) 0.00 Compare Group Senior College t-value p-value Homework 0.88(697) 0.69(204) 6.78(899) 0.00 Agriculture 0.27(705) 0.16(201) 3.15(904) 0.00 Non-agriculture 0.02(699) 0.02(200) -0.27(897) 0.79 Housework 0.36(706) 0.18(202) 4.72(906) 0.00 Play 0.46(701) 0.38(202) 2.17(901) 0.03 Compare Group Primary Senior t-value p-value Homework 0.94(3654) 0.88(697) 5.20(4349) 0.00 Agriculture 0.18(3674) 0.27(705) -5.03(4377) 0.00 Non-agriculture 0.01(3652) 0.02(699) -2.04(4349) 0.04 Housework 0.31(3668) 0.36(706) -2.40(4372) 0.02 Play 0.82(3683) 0.46(701) 21.52(4382) 0.00 Compare Group Senior Technic t-value p-value Homework 0.88(697) 0.74(286) -5.63(981) 0.00 Agriculture 0.27(705) 0.20(286) -2.11(989) 0.04 Non-agriculture 0.02(699) 0.02(285) 0.41(982) 0.68 Housework 0.36(706) 0.31(285) -1.55(989) 0.12 Play 0.48(286) 0.46(701) 0.48(985) 0.63 The sets of explanatory variables include conditions for teachers in villages (X1 the number of teachers per village, teacher salaries), the condition of educational infrastructure in the village (X2 housing condition), funding (X3 funding available in the village for education), and mode of transportation to school for students (X4 travel time to schools, transportation tools available). Table 6 presents results of the 2007 CHIP data. The R-square of the model was 5,676. The student population was determined by how many students were attending school in the villages. The more teachers in a village the higher the number of students and this resulted in a p-value of The number of part-time teachers in a village had a negative effect on the student population with a p-value of Longer travel times to middle school resulted in significant decreases in student populations and produced a p-value of.007.

12 24 Zhang and Soukup 6.4. Factors That Affect School Achievement The second set of equations determined the educational achievement of students: Here, m measured the number of years of schooling attained by students in the regression model. For the multilogit model, is a categorical variable that had six categories with 1 indicating graduation from primary school and 6 indicating graduation from graduate school. The sets of explanatory variables included parental impact parental expectations of student accomplishments), family finances ( school expenses), and how students used their time after school ( student after school activities). The 2002 CHIP survey data was applied to both the regression and the multilogit model to determine how factors affected the educational attainment of students. The results are presented in Table 7 (regression model) and Table 8 (multilogit model). The regression and multilogit analyses showed that higher parental educational expectations resulted in a significantly higher number of years of schooling completed by students (both significant at the level).regression and multilogit analyses also found that as school expenses increased the number of years completed at school by a student also significantly increased (p-value 0.000). After school student activities also significantly impacted the number of years of school completed by students. Students that performed nonagricultural chores (p-value 0.028) and childcare (p-value 0.007) after school completed significantly more years of education. Table 6 Regression Analysis of Factors that Affect the Student Population (2007) Student Population Coefficient Robust Standard Error t value p-value Number of Teachers in Village Part-time Teachers in Village Teacher Monthly Salary Total Teacher Stipend Dangerous School Houses School Funding Problems Primary School Transportation Travel Time to Primary School Middle School Transportation Travel Time to Middle School Constant Significance level is 90%. Number of Observations = 342, F (10, 331) = 16.3, Prob. > F =.0000, R-squared =.5676, Root MSE = Percentage of Children Enrolled in School Table 9shows the percentage of children enrolled in school at different school age. In primary school 83.5% (3,468 out of 4,154) of 6 to 13 year-old students, 54.4% (2,426 out of 4,459) of 12 to 16 year-old middle school students and 17.3% (640 out of 3,689) of 14 to 19 year-old senior school students were enrolled and attending school. Only 6.7% (249 out of 3,689) of 14 to 19 (2)

13 A Study of Rural Chinese Students Education 25 year-old students were enrolled in vocational schools and 9.5% (194 out of 2,058) of 16 to 23 year-old students were enrolled in college. Table 7 Regression Analysis of Factors that Affect School Achievement (2002) School Years Completed by Student Coefficient Robust Standard Error t value p-value Age Parent Expectations Student Educational Expenses After School Homework Agricultural Activities Nonagricultural Activity Nonagricultural Business Activity Afterschool Housework Afterschool Childcare Afterschool Playtime Constant Significance level is 90%. Number of Observations = 7,241, F (10, 331) = 4,706.92, Prob> F =.0000, R-squared =.8802, Root MSE = Table 8 Multilogit Analysis of Factors that Affect School Achievement (2002) School level of the Student Odds ratio Robust Standard Error z value p-value Age Parent Expectations Student Tuition Expenses Student Fee for Books Student Fee for School Uniform Other Educational Expenses After School Homework Agricultural Activities Nonagricultural Activity Nonagricultural Business Activity After-school Housework After-school Childcare After-school Playtime /cut /cut /cut /cut Significance level is 90%. Number of Observations = 3,567, LR chi2 (13) = 3,477.39, Prob> chi2=.0000, Pseudo R2 =.3975

14 26 Zhang and Soukup Table 9 The percentage of Children Enrolled in School at Different School Age (2002) Definition # of Enrollment Percentage Enrolled Children in the Age Category for Primary School (age 6-13) 4, % Children Enrolled in Primary School (age 6-13) 3,468 Children in the Age Category for Junior Middle School (age 12-16) 4, % Children Enrolled in Junior Middle School (age 12-16) 2,426 Children in the Age Category for Senior Middle School (age 14-19) 3, % Children Enrolled in Senior Middle School (age 14-19) 640 Children in the Age Category for Vocational School (age 14-19) 3, % Children Enrolled in Vocational School (age 14-19) 249 Children in the Age Category for College (age 16-23) 2, % Children Enrolled in Vocational School (age 16-23) Conclusion and Policy Implications The researchers described and analyzed the general educational environment in rural China and determined that school facilities and teacher qualities were significantly associated with school enrollment in rural China. Factors like student after school activities, parental expectations and investment in student education were highly correlated with greater amounts of educational achievement. The authors recommend that the Chinese government provides more funds to attract greater numbers of qualified faculty/administrators to teach at rural schools and more educational resources and funding for students and their families in rural areas to promote greater educational attainment. 8. Limitations Space constraints and data limitations forced the researchers to narrow the focus of the study. The researchers were only able to analyze factors that were contained in the CHIP 2002 and 2007 data sets. The data analysis focused specifically on students in rural China and may not be applicable to students in urban area of China.

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