Development Trend and Prospect of Personal Return Rate of

Vocational Education in China

Lijia Zhang

Department of Education, Moscow State University, Moscow, 119991, Russia

Keywords: Return to education, Mincer rate of return, Employment.

Abstrsct: Vocational education is one of the important ways to form human capital, and human capital is an important

factor of economic growth. Based on the data provided by CFPS, this paper uses the Mincer rate of return to

empirically analyze the development of personal rate of return of vocational education in China from 2014 to

2020. The results show that the personal rate of return of vocational education is on the rise year by year,

however, exacerbating the gap between the rich and the poor between regions and the unhealthy employment

future of vocational education educated groups are still problems that need to be solved urgently.

1 INTRODUCTION

Vocational education is regarded as an important part

of the national education system and human resources

development. It shoulders the important

responsibility of cultivating diversified talents,

inheriting technical skills and promoting employment

and entrepreneurship.

Since the 1960 s, research on the rate of return to

education has been endless. Economist Jacob in 1974.

Mincer 's income function derived from human

capital theory has become the research method of

most scholars. Regarding the research on the field of

returns to education in China, Li Shi and Ding Sai

found that the impact of education on income is

directly and positively correlated through the sample

survey data from 1990 to 1999 in ' the long-term trend

of returns to education in urban China '(Li, Ding

2003); gao Xiaochun further drew a new conclusion

in " Re-examining the Impact of Education on Income

Growth and Distribution " : Although the rate of

return to education in China showed a significant

growth trend from 1988 to 2002, the income gap

among people with different educational backgrounds

also widened. In addition, Zhou Yunbo and Yu

Yongze pointed out in the ' Main Factors Affecting

the Income Gap of Urban Residents in China ' that the

level of regional economic development, the level of

education of residents, the employment industry, the

https://orcid.org/0000-0002-7406-1426

nature of employment units and personal occupations

are the main factors affecting the income gap

between urban residents. It can be seen that although

the economic benefits brought by China 's education

are fruitful, the gap between the rich and the poor

caused by different academic qualifications is

expanding. The scale of vocational education is

comparable to that of general education and higher

education. As the main source of education for

people in rural and remote areas in China, it is of

great significance to study and analyze the current

situation of vocational education in China in recent

years (Xu 2014).

On October 12,2021, the General Office of the

State Council issued the " Opinions on Promoting the

High-quality Development of Modern Vocational

Education, " emphasizing: " We should strengthen

the characteristics of vocational education types,

improve the school-running system of industry-

education integration, innovate the school-enterprise

cooperation mechanism, deepen the reform of

education and teaching, and build a vocational

education brand with Chinese characteristics. By

2025, the characteristics of vocational education

types will be more distinct, the modern vocational

education system will be basically completed, and

the construction of a skilled society will be

comprehensively promoted.” (Xinhua News Agency

2021) Obviously, the economic benefits of

vocational education are self-evident, and it is of

506

Zhang, L.

Development Trend and Prospect of Personal Return Rate of Vocational Education in China.

DOI: 10.5220/0011914500003613

In Proceedings of the 2nd International Conference on New Media Development and Modernized Education (NMDME 2022), pages 506-512

ISBN: 978-989-758-630-9

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

great significance to explore the rate of return brought

by vocational education investment.

This paper will use the data of 2014,2016,2018

and 2020 provided by China Family Panel Studies

(CFPS) to conduct an empirical study on the

individual return rate of vocational education in

China. Through horizontal and vertical comparison,

this paper examines the current situation of education

return rate in China in the past ten years and provides

suggestions for the future development of vocational

education.

2 RESEARCH METHOD

2.1 The Calculation Method of the Rate

of Return to Education

This paper uses the Mincerian earnings function to

calculate returns on education. The Mincer income

function method is an estimation method proposed by

the American economist Mincer J in the 1970 s. The

rate of return on education estimated by this method

is called the Mincer yield. Its basic formula is :

Iny = a + bS + cEX + dEX ² + u (1)

Among them, y represents annual income, S

represents years of education, EX represents the labor

market experience, and the quadratic term of EX is

used to describe the nonlinear relationship between

labor market experience and income. A represents the

intercept, b, c, d represent the regression coefficients

of the variables, u is the random error term, where b

is the individual rate of return on education.

2.2 Data Source

The data of this paper comes from China Family

Panel Studies (CFPS), which is updated every two

years. In view of the availability of data and the

complexity of data processing, this paper selects data

from 2014 to 2020 as samples for analysis.

The data processing rules of this paper are as

follows : ( 1 ) The annual income unit is yuan, and

the magnitude is large. In order to prevent

heteroscedasticity, the annual income is

logarithmically processed, and the logarithm of the

annual income is used as a measure of the income

level. Secondly, there is no direct working life index

in the CFPS database, so the calculation of working

years is : questionnaire year-start working time ; ( 2 )

Deleting samples with missing indicator data. There

are three core variables in this paper : annual income,

years of education, and working hours. ( 3 ) delete

the abnormal data, data processing found that part of

the working life of more than 100 years, because the

data from the questionnaire, so this part of the data is

considered invalid questionnaire, delete processing ;

( 4 ) The data with annual income less than 1000 are

deleted. If the annual income is less than 1000, it is

either a questionnaire quality problem or an income

unit error. There is no way to verify the unit error and

no way to correct it one by one. ( 5 ) Due to the

existence of the maximum and minimum values that

deviate significantly from the data distribution, the

extreme values of this part will affect the accuracy of

the model. In order to avoid the influence of extreme

values on the regression results of the model, the first

1 % and the last 1 % of the indicator data are

winsorized.

The original data is cleaned and analyzed to form

a new index system, and the final modeling data

index is obtained. The data processing and data

analysis are completed by stata16.0.

Variable description is shown in Table 1.

Table 1: Variable description

Types of variables Variable names Variable symbols Variable definitions

Explained variables Income In income Logarithm of annual income

Explanatory variables

Years of education edu Current maximum years of education

Work seniority workl Year of questionnaire-year of starting work

Square of working years workl2 Square of working years

Development Trend and Prospect of Personal Return Rate of Vocational Education in China

507

3 DATA PROCESSING AND

ANALYSIS

3.1 Variable Description Statistics

Before the analysis and modeling, descriptive

statistics are carried out on the variables of the study

to grasp the overall macro situation of the variables.

The descriptive statistical results are shown in Table

Table 2: Variable Description Statistics

Va ri ab le

sampl

e size

mean

value

standard

deviatio

minimu

m value

maximu

m value

Income

19,31

3.365 2.932 0.150 17.400

Years of

educatio

19,31

10.25

4.095 0.000 18

Work

seniority

19,31

3.426 5.069 0.000 31

It can be seen from Table 2 that there are 19312

remaining samples after data cleaning, of which the

average income is CNY 3365 million, the minimum

value is 0.150, and the maximum value is 17.4. The

difference between the maximum value and the

minimum value is large, indicating that there is a

level between different individuals. Similarly, there

are great differences in the years of education and

working years of different individuals.

In order to understand whether there are

differences in income, education level and working

years between different years, the above three

variables are described and counted according to

different years. The results are shown in Table 3.

Table 3: PVariable description statistics by year

Variables

Year 2014 Year 2016 Year 2018 Year 2020

Mean

value

Standard

deviation

Mean

value

Standard

deviation

Mean

value

Standard

deviation

Mean

value

Standard

deviation

Income( ten

thousand

uan

)

2.711 2.278 3.263 2.828 3.618 3.013 4.195 3.546

Years of

education

(

ears

)

9.949 3.997 10.316 4.119 10.203 4.147 10.727 4.112

Work

seniority( years )

4.763 6.521 2.505 3.548 2.849 4.310 3.117 4.378

As can be seen from Table 3, from 2014 to 2020,

the average income and the number of years of

education have been increasing, indicating that over

time, the income level of residents has steadily

increased, the society has paid more and more

attention to education, and more people have the

ability to invest more time and money in education.

At the same time, from 2014 to 2018, the standard

deviation of income also increased year by year,

indicating that the income of residents increased at the

same time, the gap between rich and poor is also

increasing. According to the data of the National

Bureau of Statistics, the proportion of rural students

in higher vocational colleges ( 51 % in 2017 ) is

significantly higher than that of undergraduates

( 42 % in 2017 ). The monthly income of rural

graduates in 2014 is 2117 yuan after half a year, which

is slightly higher than that of migrant workers in the

same period ( 2864 yuan ). After 3 years of

graduation, the income advantage was significantly

expanded, ( that is, in 2017 ) the monthly income of

5552 yuan, significantly higher than the same period

of migrant workers ( 2017 average monthly income

of 3485 yuan ).(

Wang, Ma 2020）This shows that

higher vocational education has a significant role in

promoting rural poverty alleviation. The widening

gap between the rich and the poor from 2014 to 2018

shows that the impact of vocational education on the

poverty alleviation effect of rural students is still not

significant enough, and the society needs to pay more

attention to the development of vocational education

graduates ' workplace. However, it is worth

mentioning that the standard deviation of income in

2020 has declined compared with 2018.This may be

due to the huge impact of the epidemic in 2020 on the

job hunting of undergraduate graduates and

vocational education graduates, which is not directly

related to the economic benefits brought by

vocational education itself.

Overall, the number of years of education is also

increasing, with the improvement of people 's living

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standards, the importance of education is gradually

increasing, so the overall level of education in society

is steadily improving. From the working years, the

overall social working years have a downward trend,

due to the increase in the number of years of

education, more and more young people into the work

time delay, so this is normal.

According to the economic belt, the whole country

is divided into : eastern region, central region and

western region. The economic development of the

three regions is from strong to weak. In order to

understand the differences of the three variables in

different economic belts, the descriptive statistics

based on the economic belt are carried out. The

results are shown in table 4.

Table 4: Variable description statistics by region

Variables

Eastern region Central region Western region

Mean

value

Standard

deviation

Mean

value

Standard

deviation

Mean

value

Standard

deviation

Income ( ten thousand

yuan )

3.737 3.256 3.018 2.535 2.922 2.433

Years of education

( years )

10.555 3.989 10.271 3.936 9.634 4.378

Work seniority ( years )

3.464 5.022 3.455 5.191 3.312 5.024

As can be seen from Table 5, the annual income of

the eastern region is greater than the central region is

greater than the western region, indicating that the

more economically developed regions, the higher the

income level, the same gap between the rich and the

poor is also greater ; years of education and years of

work are the same, the eastern region is greater than

the central region is greater than the western region.

3.2 Correlation Analysis

To understand the correlation between the variables

and the explained variables, Pearson correlation test

is needed. The test results are detailed in table 5 :

Table 5: Pearson correlation test of variables

lnincome edu workl workl2

lnincome 1

edu 0.261*** 1

workl 0.110*** 0.007 1

workl2 0.052*** -0.013* 0.946*** 1

Note : * * *, * *, * indicates significant at 1 %, 5 %, 10 %

respectively

It can be seen from Table 7 that the years of

education ( edu ) and income ( lnincome ) are

significantly positively correlated at the level of 1 %,

and the correlation coefficient is 0.261, which reflects

that the investment in vocational education has a

significant positive impact on personal income. The

working hours ( workl ) and income ( lnincome ) are

significantly positively correlated at the 1 % level,

and the correlation coefficient is 0.110 ; the square

term of working hours ( workl2 ) is significantly

positively correlated with income ( lnincome ) at the

level of 1 %, and the correlation coefficient is 0.052.

3.3 Regression Analysis

Based on the conclusions of the above data analysis,

the benchmark regression analysis is performed on

all variables :

Table 6: Benchmark regression analysis

Variables

lnincome

edu 0.060***

(0.002)

workl 0.102***

(0.004)

workl2 -0.003***

(0.000)

Constant 9.206***

(0.020)

Observations 19312

R-squared 0.101

Note : * * *, * *, * indicates significant at 1 %, 5 %, 10 %

respectively, standard error in parentheses

From table 3, we can see that the regression

coefficient of edu is 0.060, and it is significant at the

significant level of 1 %, indicating that the years of

education have a significant positive impact on the

current income. The specific performance is that for

Development Trend and Prospect of Personal Return Rate of Vocational Education in China

509

every additional unit of years of education, the annual

income will increase by 0.06 units accordingly, that

is, the Mincer yield of the total sample is 0.06 ; the

regression coefficient of workl is significantly

positive at the level of 1 %, and the regression

coefficient of workl2 is significantly negative at the

level of 1 %, indicating that there is an ' inverted U '

relationship between working years and income, that

is, the annual income increases with the increase of

working years, and when the working years reach a

certain value, the income decreases with the increase

of working years. This phenomenon may be related to

the type of work unit and employment industry of

vocational education graduates.

Figure 1: Distribution of employer types of graduates of

2016 Vocational colleges

Figure 2: Distribution of emplyer types of 2017 vocational

college graduates

From Figure 1, it can be seen that the main

employment destinations of vocational education

graduates are private enterprises or self-employed

enterprises. The main characteristics of such

employers are : 1.Inborn deficiency and difficulty in

development. 2.Financing difficulties 3. Management

confusion 4.Insufficient training of talents. Such a

working environment makes it impossible for

vocational education graduates to achieve long-term

development in a fixed employer, and their working

ability cannot be improved. In addition, combined

with Figure 1 and Figure 2, it is not difficult to see

that the educated groups of vocational education are

mainly engaged in the primary and secondary

industries such as resource development, material

energy, manufacturing, or the tertiary industry of

transportation (Wang, Zhou 2018). The

characteristics of this industry are : high physical

labor, intellectual activity requirements small ; strong

substitutability ; strong instability ; high risk. This

also leads to the working income of the working

group with the increase of working age, after the peak

will decline.

3.4 Mincer Yield Comparison Based

on Different Years

In order to show the development trend of the Mincer

yield rate of China's vocational education from 2014

to 2020, the data are compared from both horizontal

and vertical aspects. The following is the comparison

results of the Mincer yield rate based on different

years :

Table 7: Mincer yield comparison based on different years

Years Mincerian rate of return

2014 0.048***

2016 0.057***

2018 0.060***

2020 0.068***

Note : * * *, * *, * indicates significant at 1 %, 5 %, 10 %

respectively

As can be seen from Table 7, the Mincer yield in

each year is significant at the 1 % level, from the

numerical point of view, the Mincer yield increased

year by year. This shows that since the 21 st century,

the field of vocational education in China has created

a great situation of reform and practice, fully serving

the development of economy, society and people, and

the development of socialist vocational education

with Chinese characteristics has made brilliant

achievements.

3.5 Mincer Yield Comparison Based

on Different Regions

The following is a comparison of Mincer yields

based on different regions :

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Table 8: Comparison of Mincer yields based on different

regions

Regions Mincerian rate of return

Eastern region 0.070***

Central region 0.049***

Western region 0.047***

Note : * * *, * *, * indicates significant at 1 %, 5 %, 10 %

respectively

From Table 8, it can be seen that the Mincer yield

rate in each region is significant at the significant

level of 1 %. From the numerical point of view, the

Mincer yield rate in the eastern region is higher than

that in the central region and higher than that in the

western region, that is, the Mincer yield rate in the

economically developed regions is higher than that in

the economically underdeveloped regions. This may

be due to the factors of many employment

opportunities, large demand for talents, perfect

infrastructure construction, good welfare treatment

and high salary in developed areas. Even in 2020,

when the whole country is seriously affected by the

epidemic and industries in various regions are hit to

varying degrees, developed regions also have stronger

economic capital to promote economic recovery,

minimize the resumption of work time, and thus

reduce economic losses.

4 CONCLUSIONS AND

RECOMMENDATIONS

4.1 Conclusion

Generally speaking, in recent years, the personal

economic benefits brought by China 's vocational

education have shown a gratifying growth trend, but

through horizontal and vertical analysis and research,

we can still find some problems in the development

of socialist vocational education with Chinese

characteristics. Firstly, the development of vocational

education fails to conform to the goal of " common

prosperity " of socialism, and the influence of

vocational education on students in underdeveloped

areas and rural areas needs to be further improved.

Secondly, the graduates trained by vocational

education cannot achieve long-term economic

benefits after employment, which may be related to

the insufficient skill level of vocational education

graduates, the unbalanced distribution of human

resources in the market, and the insufficient

investment of the state in private enterprises and

vocational education.

4.2 Recommendations

(1)To optimize the shortcomings of vocational

education development mechanism. Strengthen the

cooperation between schools and markets, establish

a sound institutional guarantee, and eliminate the

current phenomenon of some school-enterprise

cooperation and relying on " human maintenance. "

Continuously promote and improve the construction

of vocational education system, so as to better cope

with new contradictions and new problems with the

development of the times and social changes(

2019).

(2)To build a modern vocational education

system which can adapt to the current social needs

and the development of productive forces. At present,

the shortage of teachers in higher vocational colleges

is serious, and some professional teachers in

secondary vocational schools are seriously

insufficient. In addition, the lack of direct access

between secondary and higher vocational education,

and the lack of synchronization between vocational

education and general education are also the

shortcomings of vocational education itself.

However, it is gratifying that on February 23,2022,

the press conference held by the Ministry of

Education mentioned three major changes in the

relevant situation of promoting the high-quality

development of modern vocational education : the

orientation of secondary vocational education has

changed from employment-oriented to both

employment and further education ; ' Vocational

college entrance examination ' will become the main

channel of higher vocational enrollment ; further

expand vocational undergraduate education. This

means that the change of national orientation of

vocational education is likely to fundamentally solve

the problem of vocational education graduates

working conditions, so that the educated groups in

the market to obtain more favorable competitiveness

and longer-term economic guarantee.

(3)To strengthen the protection of vocational

education funding. At present, the total amount of

financial investment in China 's vocational education

is insufficient, which is significantly lower than that

of ordinary education at the same level, but its scale

is comparable to that of high school education and

higher education at the same level. Therefore,

ensuring sufficient funds for vocational education is

the basis for adhering to education development. It is

necessary to update and improve the training

equipment needed for vocational education, improve

the financial allocation system, and adopt different

allocation standards between urban and rural areas

Development Trend and Prospect of Personal Return Rate of Vocational Education in China

511

and between different regions according to different

levels of economic development, so as to promote the

modernization of vocational education (

Guangming

daily 2022).

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