Shadow education and social inequity in China- an empirical analysis based on CFPS 2018 and provincial macro data

• In Event: Social justice and equal opportunity

Proposal

1. Relevance:
Though scholars across the world reached no consistency on the efficacy of shadow education learning, they wouldn’t deny that the population participating in shadow education has increasingly spread all over the world in recent decades, especially in developing countries and East Asia. According to China Family Panel Studies, there were 28% of students in junior high schools attended different forms of extracurricular tutoring in 2010, whereas the number boomed to 72.2% in 2018. The same trend can be found in high school students, which was 34.1% in 2010 and 70% in 2018.
Most existing literature on shadow education focused on the mechanism between its participation as well as the efficacy and characteristics of the student, family as well as school, and found correlations among shadow education and factors caused by social inequity, such as student’s urbanity, family’s social economic status, parental education level, etc.. Very few research studied on some macro-level determinants of shadow education. However, as many countries such as South Korea and China seek to regulate the shadow education industry with a national executive order, it’s important to explore further macro level factors that might influence family’s decision on receiving shadow education with unequal social and economical backgrounds.

With the data from China Family Panel Survey 2018, it’s possible to locate factors that correlate to both participation and expenditure of Shadow education in Chinese primary and secondary education. Furthermore, by including statistics data from the national and provincial levels on GDP, educational investment, and social equity, the results show that not only characteristics in student, family, and school levels relevant to a family’s decision on shadow education, but regional educational inequity also play roles between provinces.

2. Theory/Context:
The human capital theory considers human capital contributes to economic development along with other capital. Commonly, formal/stream education is the one regarded as the channel to forge human capital in the educational side of investment in human capital (Schultz, 1961). However, due to the complex and intertwined relationship between private tutoring and formal education, the former also plays a significant role in forging a student’s human capital by improving his/her educational achievement and other gains that influence income in the future (Becker, 1962).
Most of the previous analyses on shadow education have focused on the micro level. However, since the Chinese government wants to regulate and control the industry with national wide executive orders (Double Alleviation in 2021), it should also consider the impact of other factors at the macro level. Xue and Zhao (2020) examined the effect of the admission rate of the college entrance examination and shadow education participation in different provinces. However, in addition to this factor, does the GDP level of each province, the level of educational investment, and the inequality of education also have an impact on the extent to which households tend to participate in shadow education?

3. Inquiry:
One unique socioeconomic phenomenon in China is that economic and educational resources are very unevenly distributed across provinces, so it may be inadequate to analyze the shadow education issue on the premise of considering the country as a whole. But very few existing papers focus on the impact of the regional level on the determinants of shadow education. Therefore, the inclusion of provincial imbalances in the analysis will provide a deeper understanding of what influences families' decision-making and will allow central and local governments to tailor their policies to the local context when faced with the shadow education problem. With this idea in mind, my research questions will be:
1. What factors affect the participation of shadow education, within and between provinces in China?
2. How does shadow education expenditure vary in different provinces?
The data at the individual level used in this study will mostly come from China Family Panel Studies (CFPS) implemented by the Chinese Social Science Survey Center of Peking University in 2018. Data at the provincial level will come from 2018 National Data from the National Bureau of Statistics, the 2018 Year Book from the Ministry of Education, and annual reports from local provincial governments.
This study will conduct an analysis by using HLM. Variables in student, family, and school levels will be put in level 1 and those in province level in level 2. For research question 1, as the dependent variable, shadow education participation is a binary variable, Hierarchical Generalized Linear Model (Bernoulli Model) will be used to detect relations between shadow education participation and variables within and between provinces. Data from students who were not participating in shadow education will be removed for question 2 and the regular Hierarchical Linear Model will be used to locate factors that could influence shadow education expenditure.

4. Findings.
Higher students’ individual educational expectations, higher family annual income, and higher parental educational year are significantly associated with higher participation and expenditure on shadow education in the past 12 months. A student’s gender and his/her academic ranking in grade are not significant to the family’s cost of private tutoring. Between provinces, higher GDP/capita, higher educational investment, lower educational Gini index, and higher university admission rate cannot predict higher participation and average expenditure on shadow education. But higher 211 university admission rate relates to students’ participation and expenditure on shadow education in that province.

5. Contribution
This research is one of the very few studies which included both micro and factors to analyze factors that would influence a family’s decision on shadow education. It would enrich the literature on shadow education with a broader view and expand the determinates from the individual level along to the regional level, and further provide governments to decrease educational inequity and regulate the shadow education industry with theoretical support.

Author

• Yuan Gao, The University Of Pittsburgh School of Education