Higher Education and Family Background: Which Really Matters to
Individual’s Socioeconomic Status Development in China
Abstract: The study found that the higher education and parents’ occupations both have a significant impact to individual’s social economic development measured by ISEI (International
Socioeconomic Index), but higher education’s impact is greater. In addition, from 1980s to
2000s, the impact of higher education has been diminishing as the access to higher education increased significantly. Data also shows that certain sectors, such as government employees,
Chinese Communist Party members, city/urban households, had a clear advantage. To our surprise, gender and minority status had no statistical difference in ISEI. Several policies were recommended to break the social stratification in the near future.
Key Words: Social Stratification in China since 1980s, Higher Education and Occupation,
Family Background and Occupation, Heckman’s Two-Stage Sampling, OLS Model, Rural
Residence and Social Mobility.
Highlights:
• Higher education has a positive and significant effect on individual’s International Socioeconomic Index (ISEI) of groups born from 1960s to 1980s in China, but the effect declines over time. • The original family also has significant effect on individual’s ISEI, but the effect is much smaller than that of higher education. • Although the higher education has been much more accessible since late 1990s, the rural residents ISEI is much lower than that of city/urban residents, while communist party members, government employees, and Eastern China region residents enjoy a premium. • Gender and minority status have no statistical differences in ISEI in this study.
1 1. Introduction
As the father of American public-school system, Horace Mann stated in 1848:
“Education then, beyond all other devices of human origin, is the great equalizer of the conditions of men, the balance-wheel of the social machinery.” Historically, education is one of the most important weapons to break the barrier of social stratification in many societies including China. Education, especially higher education has improved social economic status for people in many countries since the World War II. However, the family background is also an important factor in social economic status. A unique Chinese family background is the registered residence system (Hukou 戶口 in Chinese), which identifies everyone’s place of origin. Chinese registered residence is always the same as their parents, regardless of the actual birthplaces. Most people can only receive public education, medical care, and government jobs in their registered residence places. The registered residence is very difficult to change in China, except going to colleges and a few other limited ways.
The modern Chinese higher education started in 1977 when the National College
Entrance Examination (NCEE, or Gaokao 高考 in Chinese) was reinstated. The Chinese higher education was tuition free from 1977- 1997, and all students were heavily subsidized by the
Chinese government for their campus living cost. This opened an important channel for physical and social mobility. Higher education’s impact on social mobility has been a research topic for the last 30 years (Bian, 2002; Li, 1997; Li, 2002; Lu, 2008; Yeung, 2013; Hu and Hibel, 2014; and Hu and Hibel, 2015). But the degree of impact of the higher education compared to family
2 background, and the changes of the higher education’s impact in three distinct decades in the
1980s, 1990s, and 2000s are rarely studied. This study has two main research questions:
(1) Which matters more to an individual’s social economic status: family background, or
higher education?
(2) Has the impact of higher education on individual social economic status changed from
1980s to 2010s when the higher education in China evolved from highly selective to much
more accessible?
The following parts of this study are as follows: The second part illustrates the standard for social stratification and the method for measuring one’s socioeconomic status based on occupations; the third part puts forward two hypotheses based on literature review and reiterate the original research questions; the fourth part introduces the sources of data, samples and construction of models; the fifth part presents the results of this empirical study; and the sixth part is the conclusion and the limitations.
2. Social Stratification in China and International
Socioeconomic Index Based on Occupations
The classical work related to social stratification and occupational achievement includes
Blau and Duncan’s status attainment model in a 1967 study and they found that 20% of one’s
occupational achievements are related to family backgrounds, 80% to other factors including
individuals’ efforts and education level. Other scholars took the pragmatic approach and added
intermediary factors to Blau and Duncan’s model. For example, Sewell (1970) added such
variables as personal IQ, expectations, ambition, academic achievement, and parental
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encouragement for participation in the higher education. Turner (1960) studied cohorts on
sponsored mobility via the mass education system. There are also studies on career advancement
in communist countries. Walder (1995) argued that both education and political credentials were
needed for career advancement in China to receive “high prestige, considerable authority and
clear material privileges,” education alone will only bring high prestige but not material
privileges and authority. Szelenyi (1976) and Walder (1985) argued that the social inequity was
mainly due to state redistribution mechanism in the Soviet and its Eastern European allies, and it
was quite different from the Western market economy.
The constructivist scholars represented by Pierre Bourdieu (1977) classify three resources that determine the "social stratification" into economic, cultural, and social capital. In his view, what causes social stratification in his native country France was economic and cultural capital in the first place, while social capital plays a secondary role. The research by Chinese scholar Li
(1997) revealed that cultural capital had a cumulative effect upon academic achievement and socioeconomic status attainment. A Chinese American scholar Zhou (2014) proposed a four- capital theory in social stratification. In addition to economic, cultural, and social capitals, Zhou added a fourth—the political capital, which was defined as the individual or group’s ability to influence public policies. Martin Luther King’s civil rights movement in the 1960s built up
African American’s political capital in the United States to redress the “racial stratification”. In
2008 the African American’s political capital helped Obama to become the first African
American President, which was unimaginable in 1960s. In China and Soviet Unions, members of the communist party clearly had the political capital and typically enjoyed a career advancement premium as in studies from Szelenyi (1976) and Walder (1985).
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Carnoy and Levin (1985) put forward the view that education can break the barriers and
provide the upward mobility for the disadvantaged. Becker and Tomes (1986), develops a model
of the transmission of earnings, assets, and consumption from parents to descendants based on number of empirical studies for different countries. They found that almost all the earnings
advantages or disadvantages of ancestors are wiped out in three generations. Becker’s later
concluded that human competence, i.e. human capital, and the consequences of investments in
human competence, would be a key of children’s future earnings. In 1992, Becker won the Nobel
Prize in economics for his contribution to human-capital- earnings functions.
In summary, social stratification is determined by the ability of individuals and their
families of origin. Different from some existing researches on the impact of higher education or
family backgrounds in China (Li, 1997; Lu, 2008; Yeung, 2013; Hu and Hibel, 2014; and Hu and
Hibel, 2015), this study measures the impact of higher education on socioeconomic status for
population born in the 1960s, 1970s, and 1980s. Since Chinese college population of 17-24 years
old has been a much more homogeneous group than most Western countries, these three decades
population correspond to college graduates in three distinct time periods, 1980s, 1990s, and
2000s. The Chinese college acceptance rate was 8% in 1980, while in 2019, the rate was 62%
(China Historical College Acceptance Rate since 1977, 2020). The Chinese higher education went from highly selective to mass in just 30 years. The comparative study of three different distinct decades is a unique feature that previous studies did not address.
Since 1949, the new Chinese government has implemented a series of socialist reforms, which led to massive re-shuffling in the social status of all Chinese. It is generally agreed upon that the socioeconomic status was mainly depended on family’s political affiliation and has little or even negative association with higher education from 1949-1976. In 1977, China reinstated
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the National College Entrance Examination for people from all family background and started
market-based economy. In the next 40 years, the social classes characterized by occupation have
gradually formed in China. As Chinese scholar Bian (2002) pointed out that social classes are not
easily defined in China, and Chinese had very little inherited wealth before 1980s, but the
occupation is clearly identified in Chinese society since the 1980s. Therefore, it makes sense to
classify the socioeconomic status of Chinese people based on the individual’s occupation using a
standard coding system. However, the occupational code has no comparable value. Therefore,
this research constructs the socioeconomic status model by converting occupational code into the
International Socioeconomic Index (ISEI) set by scholars like Ganzeboom and Treiman in 1996.
The ISEI is used to convert occupations into the socioeconomic status score according to the 1988 International Standard Occupational Classification (ISCO88). ISEI considers almost all occupations from 40+ countries and sets scores based on the income, required years of training, and potential occupational prestige. As its name indicated, ISEI is an index of socioeconomic status based on occupation, not simply the income or the education. It can be used to compare, calculate, and rank different occupations. ISEI was widely used since mid-1990s and many organizations adopted it as the measurement of career status as well as family background.
OECD adopted ISEI in the Programme for International Student Assessment (PISA) based on students’ responses on parental occupation. (PISA 2003)
Here are the steps to generate ISEI based on the international standard: Firstly, based on the
ISCO88 classification of the occupational units, the optimal ratio about each occupation is scaled down to get the coefficient which bears on the occupational status related to the workers’ income and prestige. Secondly, the coefficient is used as a weight to figure out the scores for the ISCO88 units, and to estimate, transform, and reallocate the coefficients by the ISCO88 major group, the
6 minor group, and the unit group. The final ISEI scores are then put between the marks of 16 to
90 based on the ISCO88 occupational codes (Ganzeboom and Treiman, 1996). Thus, the ISEI score does not correspond to the ISCO88 occupational coding in the same scale; ISEI score is a ranking, after the coefficient adjustment, for the occupational social responsibilities and socioeconomic status. This study will be using a software from Faculty of Social and Behavioral
Sciences of Utrecht university to convert ISCO88 codes to ISEI scores. A Chinese scholar Li
(2011) suggested that ISEI based on occupational code fits the Chinese society better than many other countries and proposed the inverted T-shaped social structure in China using ISEI scores.
3. Literature Review and Research Hypotheses
Crompton (2011) summed up two views: one was proposed by Marshall and Swift (1993) who hold that the social status of one’s family of origin is more decisive upon one’s status achievement than education; the other was by Saunders (1997) who believed that compared with the social class one is born into, the skills and competence attained through education is more significant. But the two views both agreed that the inheritance of social status inequality gave some people the advantage of being superior to others without efforts of their own.
Based on the Theory of Reasoned Action (TRA), Goldthorpe (2014) holds that one’s socioeconomic status depends more on parents’ investment in children’s education. Torche
(2011) found intergenerational occupation relevance was strong among the lowly educated population. The intergenerational occupation relevance almost disappeared among people with a bachelor’s degree, but then became strong again in advanced degree holders, such as medical doctors, lawyers, and other professional degree holders. The family background and the family’s investment on higher education are not truly independent. In the U.S., college is expensive and
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the upper middle-class families’ investment in their children’s higher education is regarded as an
indispensable expenditure to sustain the children’s future status. On the other hand, families
below the middle class are less motivated to invest in their children’s higher education. The cost
of college is a higher percentage of family’s income and many low-income families don’t even
know they can get financial aid by filing the Free Application for Federal Student Aid (FAFSA)
before going to college and save their previous year’s tax records. For the first-generation college students, getting through college needs more time and effort due to lack of parental guidance. The rational decisions of families from different classes leads to the inheritance of the academic achievement inequality among classes. Thus, Restuccia and Urrutia (2004) hold that compared with the inheritance of the family’s social status, the implementation of reasonable public policy, such as need-based scholarship and free college advising for high school students, is fundamental to increase intergenerational mobility.
At the international level, based on the analysis from the data of many countries concerning the parents’ income, education, Jerrim (2015) found that education and particularly higher education is an important driver for intergenerational mobility. The research by Bjorklund and
Jantti in 2000 upon the time series data of several European countries also proved the correlation between participation in higher education and social status. Matherly (2017) analyzed the differences in the value orientation for higher education in United Arab Emirates (UAE) between students and their parents and found that the family culture and personal traits, such as of self- transcendence and self-enhancement, are intergenerational transmittable.
For studies focused on Chinese population, there has been no consistent conclusion on the degree of impacts of higher education and family backgrounds since 1980s. Using historical students' data from Peking University and Soochow University since 1949, which represent the
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national and regional universities respectively, Liang and Li et al. (2012) found that students
came from far more diverse background after 1950 compared to that before 1949; but this family
diversity has diminishing since 2000. Using the Chinese General Social Survey (CGSS) data,
many scholars, Guo and Min (2007), Zhao and Zhu (2009), Yeung and Hu (2013), and Di
(2014), hold that higher education is crucial in upward mobility, especially from the lower
classes. Using the same CGSS data, many scholars also believed that the family background still
has a significant impact. Li (2012) found the college graduates whose parents were government
officials enjoyed a 15% wage premium. Ye and Ding (2015) found that the higher education
expansion in China has diluted the value of college degree and it did not increase the social
mobility; college expansion may even lead to greater social inequality when the college tuition free program was ended in 1997.
In addition to the impact of higher education and family background on individual’s social economic status, Hu and Vargas (2015) studied the horizontal stratification among college
graduates in China. They suggested that (1) college majors in STEM, Business, Law, and other
professional degrees have significant economic advantages. (2) College ranking is correlated
with the likelihood of future managerial positions. (3) College location, the Big City Effect, is
significantly associated with salary levels after controlling for other factors. Lu (2008) using
CGSS 2003 data and found that the registered residence, or Hukou, had a significant and
persistence impact on China’s social stratification and mobility. Xiao and Bian (2018) studied
Hukou and higher education on urban jobs’ income using CGSS 2010 data set. The result
showed that rural born individuals who later received college education and had Hukou
transferred to urban/city enjoyed the highest income compared to city or rural residents with
similar college education.
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Since previous researches did not provide a conclusion whether the higher education or the family background had a stronger impact on occupation and the changes of higher education’s impact in the last 30 years, this study proposes the following two hypotheses:
Hypothesis 1: In China, compared to parents’ ISEI, the higher education has a larger impact upon one’s ISEI based on occupations.
Hypothesis 2: For Chinese population born within 1960s-1980s, the impact of higher education on individuals’ ISEI has diminished.
Sturgis (2015) found that the higher education expansion in England and Wales enhanced the average education level and reduced the occupation relevance between generations. With the expansion of higher education in China, Wu (2012) argued that flow of poor rural students with college degrees entering the upper class was common in the 1980s, but not anymore after 1990s, and the consolidation of urban college student’s elite status has been strengthened. Zhong (2013) held that higher education expansion would lead to “over education” and the solidification of classes, particularly the intergenerational inheritance of the high-income class. Yang (2016) found that it is an inadequate argument that more access to higher education is conducive to intergenerational mobility. Overall, Chinese studies did not provide convincing evidence that the expansion of higher education since 1997 promotes mobility among lower social classes.
Erikson and Goldthorpe (1992) discussed the theory of freedom related to social status change in the era of industrialization. If the decision-making on going to college only depends on one’s free choice, the college degree exerts bigger influence upon one’s socioeconomic status attainment, which bears resemblance with the market’s efficiency theory. However, going to college is never entirely a free choice. For those who had a rural registered residence in China
10 and did not have access to free high school education, the impact of higher education is distorted,
especially after 1997 when China ended its college tuition free policy, in exchange for a wider
access to higher education through rapid expansion. However, from 1980s to 2000s, the reform
of higher education from "elite" to "mass", fast development of the market economy, and
especially the increased financial aid, scholarships, and grants for poor college students, have
eliminated some, but not all, of the external constraints of going to college.1 Therefore, this study
investigates the differences of higher education’s impact upon one’s socioeconomic status from
1980s to 2000s.
4. Sampling and Econometric Models
4.1 Data sources and sampling
The data employed in this study is collected from the Chinese General Social Survey
(CGSS), jointly launched since 2003 by Hong Kong University of Science and Technology and
Renmin University of China. Samples by birth years have been collected from 28 out of 34
provinces in China. Starting 2005, both urban and rural population was included in the survey.
Initial samples totaled 32,417 households and the number of samples reached up to 35,720 in
2012. Follow-up surveys have been conducted every one or two years. Stratified multistage
cluster sampling is adopted for the survey.
ISCO-88 index for occupations have been adopted in the survey since 2010 for individuals
and their family members. The ISCO-88 can be converted to ISEIs to serve the needs of this
1 Since the college tuition was implemented in 1997, many newly admitted college students in China have dropped out. As a result, the government have stepped up efforts to waive tuition and increase financial aid for students from poor families, and many colleges and universities have opened the "green channel" to ensure poor students to study for free and receive grants to cover the basic campus expenses.
11 study. The CGSS data of 2012 and 2013 were published in 2015 and 2016. Thus, this study employs the pooled cross-sectional CGSS data of 2010, 2012 and 2013, to see the effect of higher education on individual socioeconomic status.
Table 1. Groups born in different decades Times of birth Time in university Stage in China’s higher education 1960-1969 1978-1991 Recovery 1970-1979 1988-2001 Improvement, with “985 Project” and “211 Project” 1980-1989 1999-2011 Expansion from 1999
The target population in this study is organized into three different groups by birth decades -
- 1960s, 1970s, and 1980s, in line with three different development stages of higher education in
China, namely Recovery in 1980s, Improvement in 1990s, and Expansion periods after 2000.
Samples are selected from traditional college-age individuals who were between 18 and 22 years of age as in Table 1. The group of those who were born in the 1950s serves as the reference group.
12 Table 2. Descriptive statistics at individual characteristic variables based on ISEI 2 Variables Value Sample Avg. ISEI SD Min. ISEI Max. ISEI ANOVA Size value value value F value Total 19,887 40.87 15.02 16 90 edud =1 4,551 54.35 14.09 16 90 6238.58*** =0 15,336 36.87 12.80 16 90 age b. 1950s 3,254 38.91 15.30 16 90 84.33*** b. 1960s 4,673 38.49 14.37 16 90 b. 1970s 4,918 41.45 14.82 16 90 b. 1980s 3,778 43.28 14.07 16 90 age60*edud edud=1 772 55.17 13.77 21 90 755.13*** edud=0 3,901 35.19 12.00 16 88 age70*edud edud=1 1,362 54.74 13.78 16 90 1328.13*** edud=0 3,556 36.36 11.73 16 88 age80*edud edud=1 1,590 51.97 13.52 16 90 989.53*** edud=0 2,188 36.97 10.70 16 88 lnincome 10% 441 33.70 12.06 16 85 382.96*** 25% 1,149 33.59 12.74 16 88 50% 4,515 35.27 12.70 16 88 75% 4,679 40.57 14.56 16 88 90% 4,716 45.45 15.47 16 90 public =1 2,395 54.02 17.02 16 90 2327.85*** =0 17,492 39.07 13.79 16 90 party =1 2,961 51.05 15.49 16 90 1738.13*** =0 16,926 39.09 14.21 16 90 region East 9,008 42.62 14.75 16 90 76.35*** Central 4,110 39.57 14.90 16 88 N.E. 2,314 39.61 14.86 16 90 West 4,455 39.17 15.38 16 90 gender =1 11,233 40.35 14.46 16 90 3.45 =0 8,654 41.54 15.42 16 90 minority =1 1,436 40.14 14.97 16 88 3.60 =0 18,433 40.92 15.03 16 90 Household =1 7,762 35.09 12.40 16 88 2074.31*** =0 12,123 44.56 15.38 16 90 health =1 363 36.92 14.68 16 88 25.23*** =2 1,777 38.06 15.24 16 88 =3 4,357 41.09 15.30 16 90 =4 7,842 41.10 15.01 16 90 =5 5,538 41.52 14.64 16 90 Note: 1. *, ** and *** refer to being significant at levels of 5%, 1% and 0.1% respectively. 2. Samples of some independent variables are missing. Thus, not all the variables see the number of samples the same as the total.
As shown in Table 2, the average ISEI value is 40.87 for all samples, and 54.35 for those who have received higher education. The difference of ISEI value between the two groups is
2 Refer to Appendices Table A for variable name, definition, and value.
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significant at 0.1%. In terms of birth year, those who were born in the 1980s see the highest ISEI
value at mean of 43.28, and this group is the early only-child generation in China with more
family resources. Despite slight differences in average ISEI values for groups born in different
time periods, the single-factor ANOVA still indicates inter-group significant differences at 0.1%.
Average ISEI values for those who were born in the 1960s, 1970s, and 1980s and have received
higher education are 55.17, 54.74 and 51.97, respectively, all significantly higher than those
without higher education, 35.19, 36.36 and 36.97 respectively at the level of 0.1%. This is
consistent with the result for all samples; the values of ISEI for those who have received higher education are significantly higher than those who have not, whatever decades they were born in.
Furthermore, differences in average ISEI values for those who have received higher education and those who have not stand as 19.98, 18.38 and 15.00 for groups who were born in the 1960s,
1970s and 1980s, respectively, indicating a trend of narrowing gap.
Logarithmic income is a continuous variable and described as annual income at 10%, 25%,
50%, 75% and 90% percentiles, the respective average ISEI values are 33.70, 33.59, 35.27,
40.57 and 45.45, higher as income increases, indicating the relevance of income to individual socioeconomic status.
Certain sectors have ISEI premium regardless of higher education. For those who work in the governmental institutions, their average ISEI value is 14.95 higher. Chinese Communist
Party (CCP) members’ ISEI is 11.96 higher. The premium received by CCP members and government public sector employees showed that Zhou (2014) theory of political capital applies in a communist country as well as in the United States. This result also fit what Walder (1985,
1995) found that the education credentials and the loyalty to the Communist Party are both
14 needed to have an upward social mobility in China, even after 30 years market economy and the
enormous GDP growth.
The Eastern China has been more developed, and the average ISEI value is about 3.0 or
more than the rest of China. The average ISEI value of urban households is 9.47 higher and is
significant at 0.1% level. A higher ISEI value is associated with better self-reported health
conditions, the average ISEI values are 36.92, 38.06, 41.09, 40.57, 41.10 and 41.52,
corresponding to six levels of self-reported health conditions. However, the average ISEI values
see almost no difference between ethnic minorities and the majority Han people at 40.16 and
40.92, and between male and female, at 41.54 and 40.35 respectively. We will discuss the gender
and race issue in Part 6.
4.2 Multivariate regression model and Heckman’s two-stage model
In an analysis of individual socioeconomic status, factors including educational background,
parents’ socioeconomic status and other social characteristics, and ISEI index are employed to
measure one’s socioeconomic status. Using education and parents’ ISEI as independent variables
to analyze the personal status attainment has been widely used in the social status acquisition
model and by Chinese scholars such as Li (2005). Thus, build a basic OLS model as follows:
= + + _ + + (1)
𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 𝛽𝛽0 𝛽𝛽1𝐼𝐼𝐸𝐸𝐸𝐸 𝛽𝛽2𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑓𝑓 𝛽𝛽3𝑋𝑋 𝜀𝜀𝑖𝑖 In Equation (1), EDU refers to the individual’s higher education attainment, ISEI_f refers to father’s ISEI, and X refers to characteristic variables such as demographic factors, employment, status, health and income. ISEI is converted based on ISCO-88 occupational codes. However,
ISEI may be missing because not everyone has an occupation. There is no estimate bias only if the missing variables happened randomly. However, in real life, it is not a random act to find a
15 job and stay employed. Instead, an individual chooses employment based on his/her educational background, health conditions, and many other factors. Thus, using the variable of occupational codes with non-random missing values will lead to sample selection bias. To eliminate any potential sample selection bias produced in Equation (1), this study employs Heckman’s two- stage model.
1, _ > 0 _ = (2) 0, _ ∗ 0 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑦𝑦𝑖𝑖 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑦𝑦𝑖𝑖 � ∗ 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑦𝑦𝑖𝑖 ≤ _ = + + (3) ∗ 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑦𝑦𝑖𝑖 𝛼𝛼0 𝛼𝛼1𝑇𝑇𝑖𝑖 𝑢𝑢𝑖𝑖 _ refers to the linear combination of variables decisive to an individual’s ∗ 𝑖𝑖 employment𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 decision,𝑦𝑦 _ to whether the individual is employed, to demographic
𝑖𝑖 𝑖𝑖 characteristics such as 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼employment𝑦𝑦 age, gender, and health. The resting𝑇𝑇 coefficients and random errors have similar meanings to those in previous models. Heckman’s two-stage model and the equation for inverse Mills ratio are as follows:
Stage I (4) Stage II
Inverse Mills ratio ( ) is the ratio of standard normal probability density function ( (·))
𝚤𝚤 to standard normal cumulative𝜆𝜆� density function ( (·)). When the estimated coefficient ( ) φin
Stage II is significant and not zero, there is sampleϕ selection bias. With Heckman correction,𝜌𝜌 one gets more consistent and accurate estimates.
16 5. Results of Empirical Analysis
5.1 Results of multivariate regression analysis and Heckman’s two-stage sampling
The dependent variable is the ISEI value, and the two key explanatory variables are (a)
higher education and (b) parents’ ISEI. Individual characteristics (gender, minority status, rural
resident, self-reported health status) and individual affiliations (public sector employees, CCP
members, geographical regions in China, and income) are later added into the original Model (1)
to yield Model (2) and Model (3), in order to double-check the stability of the result. The four
regions in China are geographical, they include the East, the Northeast, the Central, and the
West. The groups went through colleges in the 1980s, 1990s, and 2000s are subject to various
economic conditions. Thus, an analysis targeting different age groups is added. Standardized
coefficient estimates are used, to get comparable estimated effects for different age groups.
17 Table 3. OLS and Heckman standardized coefficients illustrating higher education’s effect on individual socioeconomic status OLS Heckman Model Model Model Model Model Model (1) (2) (3) (4) (5) (6) Edud*age50 18.73*** 17.93*** 14.68*** 18.79*** 17.91*** 14.69*** (35.70) (34.39) (27.31) (35.83) (37.73) (27.92) edud*age60 0.16 -0.66 -3.06*** 0.12 -0.64 -3.03*** (0.21) (-0.87) (-4.00) (0.16) (-0.89) (-4.01) edud*age70 -1.49* -2.64*** -4.38*** -1.57* -2.57*** -4.35*** (-2.16) (-3.84) (-6.29) (-2.28) (-3.85) (-6.28) edud*age80 -4.67*** -5.98*** -5.600*** -4.75*** -5.78*** -5.50*** (-6.64) (-8.55) (-7.80) (-6.74) (-8.36) (-7.67) ISEI_f 0.12*** 0.09*** 0.07*** 0.12*** 0.09*** 0.07*** (9.70) (7.28) (5.43) (9.77) (8.37) (5.73) ISEI_f*age60 -0.00 -0.01 -0.01 -0.01 -0.01 -0.01 (-0.22) (-0.44) (-0.53) (-0.30) (-0.66) (-0.65) ISEI_f*age70 -0.00 0.00 0.02 -0.00 -0.00 0.01 (-0.07) (0.01) (0.89) (-0.11) (-0.20) (0.78) ISEI_f*age80 -0.02 -0.01 -0.01 -0.02 -0.02 -0.01 (-0.76) (-0.73) (-0.51) (-0.81) (-0.82) (-0.56) age60 -2.99*** -2.34*** -2.82*** -2.43*** -7.32*** -5.29*** (-4.77) (-3.75) (-4.44) (-3.67) (-6.07) (-4.76) age70 -1.97** -1.24* -2.45*** -1.22 -8.40*** -5.99*** (-3.23) (-2.05) (-3.94) (-1.80) (-5.23) (-4.14) age80 -0.55 0.60 -0.37 0.31 -7.34*** -4.19** (-0.79) (0.86) (-0.51) (0.40) (-4.09) (-2.64) Individual Yes Yes Yes Yes Characteristics Individual Yes Yes Affiliations lambda 1.40* -15.34*** -7.57** (2.28) (-4.90) (-2.74) Wald chi2 6469.86 6920.59 8511.73 (P) (0.00) (0.00) (0.00) N 17794 17765 15683 32846 32826 30747 Note: 1. Except (P) for Wald chi2, all the other values in brackets are Z values; 2. *p<0.05, **p<0.01, ***p<0.001.
As shown in Table 3, with both Individual Characteristics and Individual Affiliations as covariant added in Model (3) and Model (6), the impact of higher education to individual’s ISEI is 14.68 and 14.69 respectively, significant at p<0.001 level. But the impact of parents’ ISEI-f is also significant at p<0.001, and the values both are 0.07 in Model (3) and Model (6). It shows that the key explanatory variables in the Ordinary Least Squares (OLS) and Heckman regression models have no significant directional fluctuations on the socio-economic status of individuals.
18 The result shows the stability of the effect of higher education and parent’s socio-economic
status on individual’s ISEI. The value of inverse Mills ratio “lambda” in Heckman’s Stage II is
1.40, -15.34 and -7.57 respectively, significant at 0.1% to 5% levels, indicating a necessity of
Heckman’s Model to correct sample selection bias. Compared with OLS estimates, Heckman’s
Model (4)-(6) have no inverse of coefficients of key explanatory variables, despite sample selection bias existed that prove estimation errors indeed in OLS Model (1) – (3) to some extent.
The key independent variable -- whether to receive higher education can substantially enhance one’s socioeconomic status through occupation selection. This significant positive effect has been proved in tenability testing in various models.
5.2 Testing of the Two Hypotheses
As shown in Model (6), parents’ ISEI positive effect on their children’s ISEI is significant
at 0.07, 0.06, 0.08, and 0.06 for people born in the 1950s, 1960s, 1970s, and 1980s, respectively.
An increase of one ISEI value in parental generation produces an increase of 0.07 in the next
generation’s ISEI value. Parents’ socioeconomic status is positive effect on their children’
significantly, but the effect is much smaller than the higher education’s effect on individual’s
ISEI ranging from 9.19 to 14.69. Compared with parents’ ISEI, higher education had more
substantial effects on one’s ISEI. This proves the Hypothesis One.
However, among people born in different decade, higher education has a diminishing effect,
while the impact difference of parents’ ISEI is very little.3 Compared with Model (6) with
Individual Characteristics and Individual Affiliations added, Model (5) without Individual
3 The calculation of the standardized coefficients of 60s, 70s and 80s: 0.06=0.07+ (-0.01), 0.08=0.07+ (0.01), 0.06=0.07+ (- 0.01).
19 Affiliation shows slight increase in parent socioeconomic status’ effects, but the changes are not
substantial.
Table 3 shows increases in ISEI values due to higher education, significant at 0.1%, in
whatever decades an individual was born. As shown in the Heckman’s corrected Model (6),
higher education increases ISEI values by 14.69 for those who were born in the 1950s, 11.66 for
the 1960s, 10.35 for the 1970s, and 9.19 for the 1980s.4 Compared with Model (5) with No
Individual Affiliation but only Individual Characteristics variables included, Model (6) shows a
decline in higher education’s effects on individual’s socioeconomic status of cohorts who were
born in different decades. This proves the Hypothesis Two.
Those born in the 1960s and went to university in 1980s when China’s higher education
was highly selective after the Cultural Revolution (1966-1976) when almost all colleges were
closed for 10 years. Thus, college graduates in the 1980s benefited from a “premium effect.”
Also, social strata in China were “reshuffled” during the Cultural Revolution and families from
privileged backgrounds were pushed down to the lower stratum of society; therefore, parents’
privileged socioeconomic status might not be as helpful in the 1980s.
6. Conclusions, Recommendations, and Study
Limitations
This study employs the pooled cross-sectional CGSS data of 2010, 2012 and 2013, to
analyze higher education’s effect on individuals’ attainment of socioeconomic status based on
occupations. The sampled individuals were grouped by birth decades, born in the 1960s, 1970s,
4 The calculation of the standardized coefficients of 60s, 70s and 80s: 11.66=14.69+（-3.03）, 10.34=14.69+（-4.35）, 9.19=14.69+（-5.50）.
20
and 1980s and analyzes the heterogeneous effects of higher education on socioeconomic status attainment of groups born in different decades. We conclude our research findings as below.
6.1 Higher education has a more significant effect on ISEI of groups born from 1960-1989, but the effect declines over time.
Taking account of other influencing factors, we built a multivariate regression model, and found that higher education has significant positive effect on ISEI for all groups born in different decades, and the effect declines for groups born in later decades. The result is consistent with that of the initial descriptive statistics. Heckman models reveal a positive and significant effect of higher education, but the effect fades overtime. Compared with parents’ ISEI, both models show that higher education has a larger effect on individuals’ ISEI. The positive impact of higher education on occupation since 1980 in China has been validated by many scholars. Yanjie Bian
(2002) predicted that in the post-Mao era “state redistributive inequalities are giving way to patterns increasingly generated by how individuals and groups succeed in a growing market- oriented economy.” Ye and Ding (2015) found about the key role of higher education played in breaking the social stratification in China since 1977. Higher education can change one’s career path across wide socioeconomic backgrounds and having a college degree is a common way of breaking the parents’ social stratum defined by occupation. In conclusion, our study shows that higher education has greatly improved the odds for a higher ISEI occupation.
The impact of original family on individual’s ISEI can be explained by Bourdieu’s (1977) human capital and cultural capital theory. There were also scholars who specialized in intergenerational transmission of inequality. Ludwig and Mayer (2006) had their famous study of intergenerational transmission of poverty and they argued that changing of parents culture of
21
work, marriage, and religion as a mean to change intergenerational transmission of poverty.
Solon (2017) used the concept of “intergenerational income elasticity” (IGIE) to calculate the percentage variation to expect in the child’s income in connection with a percentage variation in
the parents’ income. Based on several previous studies and empirical data, Solon concluded that
in the U.S., the IGIE is about 20%. It means if the parents’income is 50 percent above the
average in their generation, then the expected position for the child would be 10 percent (50
percent times 20%) higher than the average in the child’s generation. The impact of original
family is limited in a market economy. Yuan and Chen (2013) used Galton-Becker-Solon
equation to calculate the Inter-generation Income Elasticity (IGIE) from 1980s to 2005 based on
2006 CGSS data., the IGIE in China was at 0.49 in the 1980s, dropped to 0.25 in 1995, and went
back to 0.40 in 2005. It means the original family’s impact to individual’s income went down
until mid-1990s, but came back up since 1995.
However, the effect of higher education has declined in those 30 years. Although economic
rules of scarcity (college degrees were rare in 1980s) and diminishing returns (college degree as
an input and ISEI as the output) can explain this diminishing effect of higher education in
general, we believe that the ending of nationwide free tuition in the 1990s during the fast college
enrollment expansion may have hurt the families in the lower stratum of the society. Since the
rigid registered residence system still exists in China, people born into rural area don’t have
access to free high school as most city residents do, it is more expensive to support a college
graduate for rural household families after the college enrollment expanded in the late 1990s.
Quite different from gender and race studies in Europe and in the United States, the ISEI differences between male and female, minority and the majority Han group in China are very small and not statistically significant. We double checked our gender gap result with other
22
scholars and found Yeung (2013) actually concluded that the gender gap in college attendance
disappears and even reverses itself after the college expansion in the 1990s. The minority ISEI
issue is a surprise in our study. Most Chinese 8.49% minorities live in the Southern and Western
regions of China where the terrain is mountainous and arid. In general, Chinese minorities are
poor compared to the Han majority (National Bureau of Statistics 2011). Zhu (2010) from
Beijing Normal University studied the ethnic minority education policy in China from 1949 to
2010 and concluded that the inequity still existed for many of the 55 minority groups in China in
all education levels. However, from our data analysis, we can only say that geographic areas of
outside the Eastern China where most minorities lived did show lower ISEI values, but minority
status alone has no statistical difference. The further study of Chinese minorities’ social
stratification is beyond the scope of this study.
6.2 Recommendations to increase the social mobility after the higher
education expansion era
From Table 2, individual characteristics had major impact to ISEI. For those who work in
the public sector such as governmental institutions, their average ISEI value is 14.95 higher, a
very significant number. In China over 50% employees are on government payrolls compared to
15% in the United States (Weisenthal, 2011). In our study, we excluded the government owned
businesses, which include almost all big businesses in China. The government public sector
employees are only 12.04% of the total samples in our study, but still they have a premium of
14.95 on average ISEI value. Chinese Communist Party (CCP) members average ISEI values is
also 11.96 higher than non-CCP citizens. The average ISEI value of urban registered residence is
9.47 higher than that of their rural counterparts. The Eastern China is more developed, and the
23
average ISEI value is higher than China's Western, Central, and Northeastern regions, by about
3.0 or more. All the differences above are significant at 0.1% level.
Education as the “great equalizer of the conditions of men, the balance-wheel of the social machinery” (Mann 1848) needs joint actions of the external mechanism of the society and the internal mechanism within universities. For the Chinese society, the premiums for urban residents, government employees, and CCP members are high, each alone is similar to the advantage of college educated on ISEI. Our first recommendation is for government public sectors to treat job applicants equally based on their abilities, and not to have preference for CCP
members or city residents. In fact, the U.S. Affirmative Action policy implemented since the
1960s gave some preferences to minorities, low income families and first-generation college
students in college admissions and job applications. The Chinese government, the monopolistic
biggest employer, should take a stand of equal opportunity at minimum, or even take an
affirmative step to help the rural and low-income applicants to eliminate hereditary
socioeconomic status for government officials.
The Chinese college enrollment allocation policy traditionally favors students in big cities such as Shanghai and Beijing. The policy also favors some minorities concentrated provinces, such as Tibet and Xinjiang, but the policy has not considered the population variation and the number of colleges in each province. Our second recommendation is to allocate college enrollment numbers equitably across provinces and ensure all qualified students can afford colleges regardless family income and registered residences. Due to variations of population and the number of colleges, especially the first-class colleges in different provinces, the college
acceptance rates for populous provinces, such as Henan and Guangdong, are much lower than
24 big cities such as Shanghai and Beijing.5 The allocation of college enrollment numbers in each province is centrally administered by the Department of Education. The public policy of college
enrollment needs to be modified to help the populous provinces with large rural residents but
fewer colleges.
Our third recommendation is for colleges to establish endowment to support a strong
financial aid and scholarships program for poor students from the rural area. Using the two most
prestigious universities as an example for a strong financial aid program in the United States.
According to their university official Web site, both Harvard and Stanford offer some tuition
waiver for family income $200k or below (complete tuition free for income below $150k). Based
on U.S. IRS data and its Household Income Percentile Calculator, family income at $200,000 in
2019 is at the 91 percentiles in the U.S.. So only the top 10% families by income will pay the full
tuition at the first-class universities. The large endowment fund raised through alumni giving and
other donations not only helps poor students to apply and complete college, the endowment itself
is a symbol of a first-class university.
Last but not least, admission policies that provide extra point to students from poor rural
areas and first generation college students may be considered. In the United States highly
selective colleges, such as Harvard, the race-based admission preference has been under scrutiny
in recent years, but the admission preference for students from poor families, rural areas, and
parents without college degrees received wide support (SFFA v. Harvard 2014).
5 Souce: https://gaokao.eol.cn/.[EB/OL]-2020-8-23
25
6.3 The Limit
As for the limit of this study, it should be mentioned that the ISEI index used in this paper is
based solely on occupation. Some high-income occupations and most government jobs require a college degree. Therefore, there may be a pre-existing correlation between higher education and
the ISEI. However, because the lack of a variable to represent the individual’s true ability, any
econometric model is likely to have a certain degree of existing correlation with education,
which will result in the overestimation of the effect of education on socioeconomic status.
Moreover, this study employs pooled cross-sectional data to illustrate the differences caused by age groups, while it cannot reflect the long-term effect. A longitudinal study may be conducted based upon time-series data to analyze the long-term effect of higher education, parents’ ISEI and higher education on children’s career development and intergenerational mobility. For the age group born in the 1980s and later decades, the quality of higher education varies; graduates from 985/211 universities are different from regular 4-year university graduates, and 4-year universities are quite different from 3-year vocational colleges. College graduates are no longer a homogenous group and further stratification may help to achieve a full understanding on the effect of higher education’s quality on individual’s career and life.
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33 34 Appendices
Table A. Variable name, definition and value
Variable Definition & value Dependent variable ISEI Individual’s international socioeconomic index; value: 16-90 Key explanatory variable edud Formal higher education received and completed, get a bachelor's degree (include 3 or 4 years); Yes=1, No=0 ISEI_f Interviewee’s parents’ international socioeconomic index; value: 16-90 edun_f Interviewee’s parents’ years of schooling; value: 0-22 Occupation/identity characteristic variables lnincome Logarithm of an individual’s occupation/labor income in the last year; value: 2.08-15.61 public Post in governmental departments or other institutes in the public sector; Yes=1, No=0 Party CPC membership; Yes=1, No=0 region Region in which one works; East China=1, central China=2, northeast China=3, west China=0; west China as reference Demographic variables gender Female=1, Male=0 minority Ethnic minorities=1, Han people=0 age Interviewee’s age in the year of survey age60 Born 1960-1969; Yes=1, No=0 age70 Born 1970-1979; Yes=1, No=0 age80 Born 1980-1989; Yes=1, No=0 household Interviewee’s registered household; Agricultural household=1, Non-agricultural household=0 health Self-assessment of health; Unhealthy=1, A bit unhealthy=2, Fair=3, Healthy=4,Very healthy=5 Interaction Variable name University education*Born 1960- edud*age60 1969 University education*Born 1970- edud*age70 1979 University education*Born 1980- edud*age80 1989 Parents’ ISEI* Born 1960-1969 ISEI_f*age60 Parents’ ISEI* Born 1970-1979 ISEI_f*age70 Parents’ ISEI* Born 1980-1989 ISEI_f*age80 Note: 1. Reference group for the dummy variable of age, i.e. age 60, age 70 and age 80, is the group born between 1950 and 1959; 2. The four areas in “region” variable are demarcated in line with the four economic regions in China, namely east, northeast, central and west China. East China: Beijing, Tianjin, Shanghai, and Hebei, Zhejiang, Jiangsu, Fujian, Shandong, Guangdong and Hainan provinces. Central China: Henan, Shanxi, Anhui, Jiangxi, Hubei and Hunan provinces. Northeast China: Liaoning, Heilongjiang and Jilin provinces. West China as reference group: Inner Mongol Autonomous Region, Guangxi Zhuang Autonomous Region, Chongqing, Xinjiang Uyghur Autonomous Region, Ningxia Hui Autonomous Region, and Gansu, Qinghai, Yunnan, Guizhou, Shaanxi and Sichuan provinces, as well as Tibet Autonomous Region.
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