WAGE Returns to Education under Different Levels of Higher

Education based on Big Data Analysis

Jing Wang

1,2 a

and Hui Zhang

School of Economics and Management, Wuhan University, Luojia Street, Wuhan, China

Economics, The University of Sheffield, Sheffield, UK.

Keywords: Wage, Higher Education, 2SLS, Endogeneity Test, Big Data.

Abstract: In recent years, the rapid growth of the internet has brought about an era of big data, bringing opportunities,

challenges and changes to both higher education and people's income levels. The labour market and the

education market are closely linked and the level of education is crucial to a country's economic development.

This paper uses data from CLDS 2018 and regression analysis method in big data analysis to argue for a

relationship between them and to test for endogeneity. The findings show that there is a significant positive

correlation between the level of higher education and wage, and this feature will be maintained over time.

Therefore, the country and government should focus on how to make higher education more accessible and

should make higher levels of higher education accessible to those in the labour market.

1 INTRODUCTION

The current era is the era of big data, in which

artificial intelligence is becoming more and more

developed. In addition to driving economic growth, it

also poses a huge challenge to the modern labour

market, which requires people in the labour market to

have a higher level of education in order to take

advantage. According to the National Bureau of

Statistics of China, since 1995, when the development

strategy of "developing the country through science

and education" was proposed, the national financial

expenditure on education has risen from RMB

1,411,523,300,000 in 1995 to RMB 400,465,500,000

in 2019, that is, an increase of 183.79%. Figure 1

shows the number of students who received higher

education for the six years from 2015 to 2020, from

which it can be found that the number of people who

can receive higher education in China is increasing

year by year, and the scale of higher education is

expanding rapidly, which makes Chinese higher

education change from elite education to mass

education and increases people's access to receive

higher education, which can satisfy the needs of the

development of the times. Figure 2 shows the number

of graduates who have received higher education

https://orcid.org/0000-0003-2338-4444

https://orcid.org/0000-0003-2377-4000

from 2010 to 2021, with the number of higher

education graduates in 2021 being approximately

1.58 times that of 2010, and the overall trend predicts

that in the future labour market, the number of the

graduates who have received higher education in the

labour force is expected to increase in the future.

However, the quality of higher education has

become one of the major concerns of scholars.

According to the National Bureau of Statistics of

China, as of 2019, the number of doctoral graduates

in China is 625,780,000, the number of master's

graduates is 577,088,000, the number of

undergraduate graduates is 3,947,157,000, and the

number of college graduates is 3,638,142,000, and

from the analysis of the data, the college graduates

account for 44% of the total graduates who have

received higher education, undergraduate graduates

account for 48% of the total graduates, the number of

master's degree graduates accounts for 7% of the total

number of university graduates, while doctoral

graduates only account for 1% of the total number of

university graduates. This shows that although there

are many graduates with higher education in the labor

market, the higher the level of education, the smaller

the number of graduates, and the doctor degree is

definitely at the top of the education pyramid.

Wang, J. and Zhang, H.

Wage Returns to Education under Different Levels of Higher Education based on Big Data Analysis.

DOI: 10.5220/0011360600003440

In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022), pages 979-985

ISBN: 978-989-758-593-7

979

Currently, most of the papers on the study of

higher education attainment levels and their income

in China are from the perspective of the theoretical

foundations of pedagogy, with less empirical research

on the relationship between the two. Therefore, this

paper will analyse and study the relationship between

people with higher education levels and their wage in

China based on the context of big data analysis, using

data from the 2018 China Labour Force Dynamics

Survey and an empirical research approach.

Figure 1: The number of higher education enrolment.

Figure 2: The number of college graduates in China.

2 LITERATURE REVIEW

With the development of the Big Data era, higher

education institutions can use this opportunity to

enhance the education of their students and provide

innovative educational experiences (Huda, et al.,

2016). In his thesis, Kang (Kang 2004) integrated and

summarised the whole process of higher education

reform in China since the founding of New China, in

which the state also focused on the balanced

development of quantity and quality among regions,

for example, transferring educational resources to the

western region, and at the same time, the reform of

higher education also brought about rapid economic

development. Lee et.al. (Lee, et.al, 2015) argued that

access to higher education may actually be a risky

investment. They develop a model in which they state

that the income return to higher education is not the

same for each individual, as it is a continuum: firstly,

to determine whether it is possible to enter university

for higher education, and secondly, whether it is

possible to successfully complete higher education

and obtain the corresponding degree. Walker and Zhu

(Walker, Zhu, 2008) used cross-sectional data from

the 1994-2006 Quarterly Labour Force Survey in the

UK to examine how the sharp increase in higher

education graduates in the UK would affect the level

of the wage premium and conclude that, although

there are large fluctuations in the results of the

empirical study, the relatively small scale of the

significant increase in university higher education

graduates The increase in the number of graduates

from university higher education did not result in a

significant wage premium due to their relatively small

size. In the study, Fortin (Fortin 2006) pointed out

that there is a strong link between policies on higher

education, the number of students enrolled in higher

education and wages between states, as there is some

variation between states in the US. Livanos and

Nunez (Livanos, Nunez, 2012) comparatively

analysed differences in earnings returns to higher

education by gender using data from case and labour

force surveys in Greece and the UK and using Oxaca-

Blinder's decomposition method. The results of the

study found that most of the differences in higher

education graduates in Greece and the UK could be

explained, with only a very small number of

unexplained reasons. In his study, Zhong (Zhong

2011) pointed out that most of the studies on higher

education and return on income nowadays have

mostly used people's years of schooling as a measure

of the level of education received, and few have

studied the relationship between the quality of higher

education and return on income from higher

education, and most of the studies have focused on

developed countries such as the UK and the US. In

his study, he therefore examines the relationship

between the quality of higher education and its return

to income using OLS regression analysis, using the

quality of higher education as an indicator, in the

context of China, the world's largest developing

country. Fang (Fang 2012) conducted an empirical

study of higher education schools with transnational

higher education programmes in China, comparing

the differences between research and teaching

universities. Colclough et.al. (Colclough, et.al, 2010)

pointed out that although education does not have a

direct interest in the market, it can help people to be

more productive and earn more in the labour market,

thus indirectly creating social productivity and

generating wage returns for people. Asadullah et.al.

(Asadullah, et.al, 2020) studied the returns to

education in the Chinese labour market using data

from two rounds of the Chinese General Social

Survey and based on Mincer's income equation and

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980

the least squares (OLS) method, which showed that

those with higher education degrees had higher

earnings from education.

From the above studies, it can be tentatively

concluded that there exists a strong link between

higher education and wage returns, and that the two

are positively related. Most of the existing studies

have examined the relationship between years of

education and wage return earnings, and there is some

literature on the relationship between education levels

and labour market wage earnings in China, but most

studies have compared the difference in wage returns

between primary and tertiary education. With the

reform of higher education in China in recent years,

more and more people have been able to access higher

education, higher education has become universal,

and the wage income levels of those who have

received higher education are significantly higher

than those who have only received primary education,

so more attention should be paid to studying the

relationship between higher education and wage

income returns. However, there are different

classifications and standards for the quality of higher

education in China, and existing studies do not take

into account the actual national context of China.

Therefore, this thesis classifies the level of higher

education according to four different levels: college,

undergraduate, master and doctor, according to the

actual situation in China. In addition, the traditional

'education-income' model does not take into account

the endogeneity of education, so this paper uses an

instrumental variables approach to correct for

endogeneity.

3 DATA AND METHODOLOGY

3.1 Data

The data used in this paper is the China Labour Force

Dynamics Survey data included in the 2018 survey by

the Social Science Research Centre of Sun Yat-sen

University, referred to as CLDS 2018. The China

Labour Force Survey is a project started by Sun Yat-

sen University since 2012, and this project is a

biennial tracking survey of urban and rural residents

in China, covering individuals, households and

communities in almost all provinces of China (except

Taiwan Province and Tibet), and the coverage of the

survey includes the education level, employment and

income of the respondents, and the data are cross-

sectional. The CLDS study used a round-tripping

questionnaire in which the sample was randomly

divided into four sections, which were followed for a

total of six years and then updated. The data structure

of this survey can be roughly divided into six layers:

information about the individual's community,

information about the individual's family, basic

information about the individual and his/her parents,

information about the individual's work, information

about the individual's history and some other

information about the individual. The relationship

between higher education qualifications and wage

returns is the subject of this study, and the survey

includes the qualifications of the individual

respondents, which meets the needs of this study. A

total of 16,537 respondents were included in the

CLDS2018 data, and after excluding some missing

samples, the study data for this paper is 1,480.

3.2 Methodology

The underlying model used in this paper is the Mincer

income equation model, which can be expressed by

the following equation.

lnwage=α+β

E+β

S+β

exp+β

exp

+γZ+ε (1)

The following are the meanings of the expressions

in the formula. The first variable lnwage represents the

logarithm of the respondent's wage and the wage

chosen is the wage level given in the database for

2017. S indicates the number of years of education of

the respondent, but the database chosen does not give

the number of years of education of the respondent

directly, so it should be calculated using equation (3).

represents the wage returns to different higher

education qualifications, β

is a coefficient on years of

education, β

is a coefficient on years of work, and β

is a coefficient on the square of years of work. E

represents the different levels of education in higher

education and exp represents the work experience of

the respondents, but as work experience is not

measurable, the number of years the respondents have

worked was chosen as a measure of work experience,

and exp2 represents the squared term of work

experience, Z is some other control variable and ε is

the residual term. However, the years of work is also

not given directly in the database of CLDS2018.

Therefore, it needs to be calculated by equation (4) to

obtain it.

age=2018-birth year (2)

S=Highest degree graduation yea

irth yea

-6 (3)

Of the three additional equations, equation (2) is

used to calculate the age of the respondents, as the

Wage Returns to Education under Different Levels of Higher Education based on Big Data Analysis

981

database used in this paper is from 2018, and therefore

the age of the respondents in the context of the

prevailing environment is 2018 minus their respective

year of birth, which is represented in this paper as birth

year. Equation (3) is used to calculate the years of

education of the respondents. In China, people's

formal education starts at the age of 6, so the years of

education need to be subtracted by 6 from the end of

the highest education minus the year of birth. Equation

(4) is used to calculate the years of work of the

respondent, the principle is to use the respondent's age

first minus the years of education, as the years of

education does not include the period of time before

the individual is 6 years old, so you need to calculate

the result on the result minus 6, so the final result to

get the years of work of the respondent, used to

represent the work experience of the respondent.

Overall, the idea of this paper is to use a stepwise

regression approach, adding the four control variables

in turn to obtain the best-fit equation, and then observe

the validity of the model and the change in the

coefficients to obtain a relationship regarding the

relationship between higher education qualifications

and wage returns. Finally, as education is somewhat

endogenous, the paper subsequently uses the

educational attainment of the respondent's mother as

an instrumental variable to correct for endogeneity.

3.3 Descriptive Statistics

Table 1 provides the summary statistics of the dataset.

As can be seen from the table 1, the mean of the

logarithm of wage returns is 10.855, a figure that is not

significantly different from the median figure, a result

that indicates a relatively even distribution of income

return receipts among the respondents in the database.

Figure 3 shows a box plot of the logarithm of higher

education levels on wage returns for different levels of

education, from which the following results can be

found. The first is that it is clear from the figure that

the average level of wage returns by qualification

tends to increase with higher education qualifications,

for example respondents with a doctor degree have a

significantly higher average level of wage returns than

respondents with other qualifications. Secondly, in

terms of the distance between the upper and lower

quartiles, the box plot for PhD qualifications has the

smallest distance between the upper and lower

quartiles, indicating the most concentrated distribution

of wages, followed by masters, undergraduates and

colleges in that order. Figure 4 shows the Kernel wage

distribution for different levels of tertiary education.

From the figure, it can be seen that the trends of the

curves of the Kernel wage distribution for the four

higher education levels are broadly similar, but the

peaks appear at different locations for each

qualification, indicating that each higher education

level corresponds to a different probability density of

wage returns.

Table 1: Summary statistics.

mean sd min max

Edu 1.568 0.622 0 4

S 10422 30611

98013

9838

exp -10520 30359

97981

exp2 1.03e+09 2.97e+09 0 9.6e+09

lnwage 10.855 0.939 1.609 14.88

gender 0.497 0.500 0 1

lnWage 0 1.000 -9.85 4.288

lnS 3.823 2.675 1.099 11.49

Exp 0 1.000 -2.88 0.348

lnexp2 6.321 6.118 0 22.99

Figure 3: lnwage box plot at different levels of higher

education.

Figure 4: Kernel wage distribution in different levels of

higher education

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4 RESULTS

4.1 Analysis of OLS Regression Results

As it can be seen from the previous analysis, there are

multiple control variables in this study, so this paper

has chosen a stepwise regression analysis to

investigate the relationship between higher education

qualifications and wage earnings by adding control

variables one by one. The estimation results in Table

2 demonstrate the impact of four different tertiary

education qualifications on wage earnings under

different control variables, and allow the robustness of

the regressions to be analysed in the light of the

results. The control variables selected in this paper are

lnS, Exp, lnexp2 and gender, which are added to

models 2, 3, 4 and 5 in turn. These five models are

denoted as M1, M2, M3, M4 and M5 respectively, and

M5 is the final result presented after the addition of the

four control variables. As can be seen from the table,

the regression coefficient for M1 is 0.264, expressing

the implication that after controlling for the remaining

four control variables, there is a positive relationship

between the respondents' level of tertiary education

and their wage returns, and that for each level of

tertiary education, their average wage returns increase

by 26.4%. The regression coefficient for educational

attainment is gradually increasing with the inclusion

of the control variables. At M5, the regression

coefficient is 0.324, indicating that with the inclusion

of all of the paper's control variables, their average

wage return increases by 32.4% when each level of

higher education qualification is increased. R-sq in the

table is the square of R, which represents the fit of each

model, i.e. the percentage change in the dependent

variable for the fitted model, and it can be seen from

the table that the overall model has a perfect good fit.

The F-values in the table represent the F-statistic,

which is a variance test for the validity of each

corresponding model. Based on the F-values obtained

in the table it can be seen that the F-values for models

1 to 5 are all statistically significant at the 1% level

and therefore all five models are valid. The results of

this stepwise regression illustrate that an increase in

higher education qualifications does have a positively

correlated contribution to wage returns.

Table 2: OLS estimation of the effect higher education

quality on wage returns.

Var M 1 M 2 M 3 M 4 M 5

Edu

0.264

***

0.298

***

0.298

***

0.330

***

0.324

***

(6.40) (7.00) (6.96) (7.73) (7.65)

lnS

0.023

***

0.034 0.061 0.026

(2.36) (0.32) (0.58) (0.25)

Exp 0.030

0.684

***

0.534

(0.11) (2.28) (1.79)

exp2

0.100

***

0.090

***

(6.54) (5.91)

gend

0.276

***

(5.44)

_cons

-0.419

***

-0.544

***

-0.586

-1.355

***

-1.287

***

(-5.96) (-6.54) (-1.45) (-3.19) (-3.06)

N 1480 1427 1427 1396 1396

R-sq 0.026 0.034 0.034 0.061 0.079

41.016

26.405

17.595

23.473

25.081

t statistics in parentheses

* p<0.1, ** p<0.05, *** p<0.01

4.2 The Result of 2SLS

In the case of the education-income model, the

endogeneity of education arises because of omitted

variable errors. In practice, it can be found that even

if two people have the same years of education and

the same work experience, there is still a certain

difference in their salaries, which is caused to a large

extent by the different "abilities" of the two people,

for example, one is more efficient than the other. The

reason for this is to a large extent that two people have

different "abilities", for example, one is more

efficient than the other, so it is not really possible to

determine whether the person with the higher salary

has a higher level of education or whether it is

because he is more competent. In this paper, the

instrumental variables approach will be used to

address the issue of endogeneity. Based on the data

available in CLDS2018, the household location of the

respondents was selected as an instrumental variable

to correct for educational endogeneity. Table 3 show

the results of the 2SLS regression. The Hausman test

was first conducted and the result was less than 0.005,

so the original hypothesis of "all explanatory

variables are exogenous" can be rejected, that is, there

Wage Returns to Education under Different Levels of Higher Education based on Big Data Analysis

983

are endogenous explanatory variables. The F-value of

the weak instrumental variables was then tested, and

was greater than 10, so the original hypothesis could

be rejected. In addition, according to the first stage

regression results of 2SLS, the respondents'

household location was positively correlated with the

level of higher education they could receive,

indicating that the level of higher education that those

living in urban areas could receive was significantly

higher than those living in rural areas. According to

the results of the second stage of the 2SLS regression,

the conclusions obtained from the 2SLS are

consistent with the OLS findings, that is, the higher

the level of higher education that people can receive

has a significant positive correlation with wage

income.

Table 3: The result of 2SLS.

2SLS results

First-stage regression (explained

variable: Edu)--Household

registration

0.08

Second-stage regression (explained

variable: lnwa

)

--Hi

her education

1.628

1076

0.0448

Weak identification test——F value

10.928

Hausman test——Prob>chi2

0.0042

5 CONCLUSION

The findings of this paper are as follows. Firstly,

higher education has become more common in China

and more and more people are able to access it, but

the quality of the higher education people receive is

still the point of greatest concern for the state and the

people who need it. In this context, the quality of

higher education can have two meanings: one is the

level of qualifications in higher education and the

other is the quality of the schools in which higher

education is offered. In recent years, Chinese

economy has been growing faster and faster, and this

faster economic growth relies heavily on the level of

education of the workforce. The more educated

workers are, the quicker they can integrate into the

labour market, thus reducing training costs to a

certain extent and generating greater returns in the

labour market. Therefore, in the future development

of higher education in China, we should not only

focus on quantitative growth, but also on quality

education for students. Secondly, it is important for

individuals and families to be properly aware of the

level of importance of investment in higher education

as well as the quality of higher education. According

to the empirical results of this paper, the higher the

level of quality of higher education received, the

greater will be their future wage return income. It is

therefore important for individuals and families to

take a longer-term view, recognise the future benefits

of education and achieve long-term education and

sustainability of their own education. What is more,

regional differences have a large impact on access to

higher education, and there are two aspects to

regional differences: urban-rural differences and

differences between cities. To address this problem,

the country and government should introduce

relevant policies and incentives to allocate more

quality teachers to teach in rural areas, and provide

more education funds to rural areas so that they have

better education resources than they do now; at the

same time, they should also focus on the development

and balanced distribution of higher education

between provinces, and establish more higher

education schools in provinces that currently have

fewer higher education schools. enabling students to

have greater access to higher levels of higher

education. Finally, the findings of this paper confirm

that there is indeed a close relationship between the

quality of higher education and wage returns, which

also suggests that there is also a close relationship

between the further development of China's future

labour market and people's higher education

qualifications. The current era is the era of big data

and artificial intelligence. Some of the more basic

jobs may be replaced by artificial intelligence step by

step in the future, and more people may face the

dilemma of unemployment. But it also requires us to

focus on the development of our own education and

not to stick to the status quo, but to keep learning new

skills to cope with the trends and developments in the

world. At the same time, the state should also pay

more attention to the cultivation of the quality of

human capital. To make people's lives better in the

future, it should devote itself to raising the level of

people's education quality, and should also narrow the

gap in the uneven distribution of educational

resources, for example, by assigning more excellent

teachers to remote places such as the West, raising the

level of welfare, attracting more talents to teach in the

countryside, and raising the overall level of higher

education in China step by step to ensure the stable

development of the economy.

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