Does Husband's Education Level Affect Wife's Employment

Participation? Mediation Effect Test based on CGSS

Yang Shi

Department of Accounting Shandong Youth University of Political Science Jinan, 250103, China

Keywords: Husband's Education Level, Gross Monthly Income, Wife's Employment Participation, Mediation Effect Test,

Big Data Statistics Analysis.

Abstract: In reality, women's employment participation is affected by various factors. Using the data of CGSS, through

the micro-econometric model, we use computer statistical software—Stata, to test the effect of the husband's

education level on the wife's employment participation through the mediating variable--personal gross

monthly income. The study shows that: the husband's education level has a significant positive impact on his

gross monthly income; only when the husband's education level is above college, the mediating effect is

established, that is, only the husband has received above college education, then can positively affect wife's

employment participation. In order to increase the employment participation of wives, the joint efforts of

many parties are needed.

1 INTRODUCTION

In reality, married women's employment participation

is affected by various factors (Xiang 2019). With the

gradual improvement of the education level of indi-

viduals in modern society, couples are also more

well-matched in education (Zheng 2020). This article

takes the husband's education level as the starting

point, focuses on the wife's employment participa-

tion, and uses micro-data and econometric methods to

explore the relationship between the husband's edu-

cation level and the wife's employment participation,

then tries to answer the following questions:

First, does the husband's education level affect his

personal income?

Second, does the husband's education level affect

the wife's employment participation through his per-

sonal income?

The labor force participation rate of the female

population in our country has always remained at a

relatively high level

(Zheng 2020), therefore, the em-

ployment participation of women, especially married

women, has an important impact on economic devel-

opment. Relying on big data, using computer statisti-

cal software to study the most basic micro-family de-

cision-making on employment participation of cou-

ples, has important practical significance for improv-

ing labor market policies and promoting true equality

between men and women.

2 VARIABLE SELECTION AND

MODEL BUILDING

2.1 Data Sources

The article selects the data of the Chinese General So-

cial Survey (CGSS) in 2015， which started in 2003

and is the earliest national, com-prehensive and con-

tinuous academic survey project in China, covering

28 provinces (municipalities, autonomous Region),

involving information at multiple levels such as soci-

ety, community, family, and individual. The survey

objects are adults over the age of 18. The total number

of samples in CGSS2015 is 10,968. According to the

research purpose of the article, married males were

selected as the sample, and the samples with missing

key variables in the survey year were excluded, then

finally 2171 samples were obtained.

Shi, Y.

Does Husbandâ

Zs Education Level Affect Wifeâ

Zs Employment Participation? Mediation Effect Test Based on CGSS.

DOI: 10.5220/0011897900003613

In Proceedings of the 2nd International Conference on New Media Development and Modernized Education (NMDME 2022), pages 51-56

ISBN: 978-989-758-630-9

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

2.2 Variable Selection

2.2.1 Wife's Employment Participation

Wife's employment participation is the main ex-

plained variable. In CGSS2015, the question concern-

ing wife's employment participation is: “What was

the employment status of your spouse or common-

law partner in the last week?” There are four options.

Excluding the samples that selected items 2 and 3,

items 1 and 4 are reserved as the basis for assignment

of employment participation. If you select item 1 “not

engaged in any work for the purpose of obtaining eco-

nomic income”, the wife's employment participation

is assigned a value of 0; If you select the item 4 “en-

gaged in work for the purpose of obtaining economic

income (including joining the army)”, then the wife's

employment participation is assigned a value of 1,

that is, the explained variable is a binary variable.

2.2.2 The Education Level of the Husband

The core explanatory variable of the article is the ed-

ucation level of the husband. The highest level of ed-

ucation (including current students) of the sample was

selected as the education level of the husband, and it

was divided into seven levels, and the illiterate group

was used as the control group.

2.2.3 Husband’s Gross Monthly Income

According to the research purpose of the article, the

gross monthly income (GMI) of the sample was se-

lected as the mediating variable, which is obtained by

dividing the annual total income by 12, and the loga-

rithm of the GMI is smoothed as the mediating varia-

ble of the model.

2.2.4 Control Variables

In addition to the above, the article selects the sam-

ple's monthly working hours (weekly working

hours*4), age, nationality, household registration, po-

litical status, and work ownership as control varia-

bles.

2.3 Definition of Each Variable and

Descriptive Statistics

The descriptive statistics of each variable (except for

control variables) are shown in the following table 1.

Table 1. Descriptive statistics of variables

Variable Definition and Assignment Min Max Mean

Number of

Samples

Wife's

employ-

ment

participa-

tion

Engaged in work for the purpose of obtaining eco-

nomic income in the past week, assign a value of 1

0 1 0.59

1283

Not engaged in any work for the purpose of eco-

nomic income in the last week, assign a value of 0

888

Primary

school

Primary school is 1; non-primary school is 0 0 1 0.22 476

Junior

high

school

Junior high school is 1; non-junior high school is 0 0 1 0.35 753

High

school

High school is 1, including vocational high school,

ordinary high school, technical secondary school

and technical school; non-high school is 0

0 1 0.21 459

College

College is 1, including college (adult higher educa-

tion), college (formal higher education); non-col-

lege is 0

0 1 0.08 180

Under-

graduate

Undergraduate is 1, including undergraduate (adult

higher education), undergraduate (formal higher ed-

ucation); non-undergraduate is 0

0 1 0.08 170

Postgrad-

uate

Postgraduate or above is 1; non-postgraduate or

above is 0

0 1 0.01 22

GMI lo

Gross annual income/12 3.51 13.63 7.68 2171

Note: In the above table, except for the wife's employment participation in the first row, the rest of the variables are descriptive statistics of

the husband's sample.

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According to Table 1, among the screening sam-

ples, the employment participation rate of wives is

only 59.1%, which is not particularly high. The pos-

sible reason for this situation is that the age of the

wives of the sample is not specially screened, that is,

a sample of all age groups that meet the conditions is

selected, including retirement. The reason for this

treatment is that although the current retirement age

in our country is 60 years old for men and 55 years

old for women, because the question is “spouse's

work situation in the past week”, even if the wife has

retired, The short-term employment situation of re-

turning part-time jobs is also common, so the age

screening of wives is not considered here, and a sam-

ple of all ages is used.

The highest educational background of the se-

lected sample is mainly junior high school and high

school, and only 372 have received college education

or above, accounting for only 17.13% of the whole

sample.

There are only 22 with a postgraduate degree

or above, accounting for only 1%. This shows that alt-

hough our country's university enrollment has been

expanding, the proportion of people who have re-

ceived college education or above is still relatively

small, especially the shortage of high-level talents

above postgraduate level.

Other control variables. The samples are mainly

Han nationality, and the age group is mainly young

and middle-aged; the agricultural household registra-

tion samples are mostly; the party members are few;

especially the samples engaged in “good jobs within

the system” in the traditional sense are few.

2.4 Model Building

According to the research purpose of the article and

the practice of Wen Zhonglin and Ye Baojuan (2014)

(Wen, Ye, 2014), the following model(1)(2)(3) are

constructed for verification:

𝑊𝑜𝑟𝑘=𝛼



+𝛽



𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛+𝛾



𝑋



+𝜇



(1)

𝑌=𝛼



+𝛽



𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛+𝛾



𝑋



+𝜇



(2)

𝑊𝑜𝑟𝑘=𝛼



+𝛽



𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛+𝛿𝑌 + 𝛾



𝑋



+𝜇



(3)

According to model (1)(2)(3), 𝑊𝑜𝑟𝑘 represents

the employment participation of the wife, which is the

explained variable, and is a binary variable; 𝑌 repre-

sents the mediating variable, the GMI of the husband

(logarithmic form), 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 represents the ex-

planatory variable--the education level of the hus-

band, which is a multi-category variable; 𝑋



repre-

sents the control variable. 𝛽



,𝛾



,𝛿 are parameters to

be estimated, 𝜇



is random errorterm, and 𝛼



is a

constant term.

Wen Zhonglin and Ye Baojuan (2014) (Wen, Ye,

2014) pointed out that the mediation effect test should

be tested in sequence first, that is, for the above

model, if at least one of 𝛽



and δ is found to be insig-

nificant, the Sobel test is required. If both 𝛽



and 𝛿

are significant, the result of the sequential test is

stronger than that of the Bootstrap method. Therefore,

the standards for judging the mediation effect of the

above model are: the explained variable 𝑊𝑜𝑟𝑘 has a

regression on the explanatory variable 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛,

and the coefficient 𝛽



is significant; the mediating

variable 𝑌 has a regression on the explanatory varia-

ble 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛, the explained variable 𝑊𝑜𝑟𝑘 has a

regression on the mediating variable 𝑌 and the ex-

planatory variable 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛, if the coefficients 𝛽



and 𝛿 are both significant, it means that there is a me-

diating effect.

3 METHODS: ECONOMETRIC

TEST OF THE MEDIATION

EFFECT

According to the above-mentioned mediation effect

test method, this paper intends to use Stata to test the

significance of the regression coefficients of the con-

structed models respectively. Stata is a set of com-

puter statistics software that relies on big data to ana-

lyze data, manage data, and draw professional charts

for users. Stata is powerful and a very convenient

computer software.

3.1 A Test of the Effect of Husband's

Education Level on Wife's

Employment Participation

Use binary logistic regression to test the significance

of the regression coefficients of the model (1). The

regression results are as follows (Table 2):

Does Husbandâ

Zs Education Level Affect Wifeâ

Zs Employment Participation? Mediation Effect Test Based on CGSS

Table 2. Logistic regression results of model (1)

lanator

Variable Coefficient Standard Erro

lanator

Variable Coefficient Standard Erro

Primary school

-0.367*

（2.827）

0.218 Age

-0.015***

（12.708）

0.004

Junior high school

-0.342

（2.446）

0.219 Nationality

-0.240

（2.045）

0.168

High school

-0.160

（0.465）

0.235

Household

registration

-0.032

（0.073）

0.118

College

0.494*

（2.945）

0.288 Political status

-0.187

(1.770)

0.141

Undergraduate

0.738**

（5.869）

0.304 Ownership

-0.303**

（4.749）

0.139

Postgraduate

1.419**

（4.431）

0.674 Constant

1.299***

（11.678）

0.380

Monthly working

hours

0.001**

（3.822）

0.001

Hosmer and

Lemeshow test

0.375

Note: The data in parentheses are wals values, ***, **, * indicate significance at the 1%, 5%, and 10% confidence levels, respectively.

For the Hosmer and Lemeshow test of the above

binary logistic regression, the P value is 0.375>0.05,

indicating that the model (1) has a good fit. The re-

gression results show that, for the education level of

the husband, primary school,college, undergraduate

and postgraduate all have a significant impact on the

wife's employment participation. The difference is

that the education level of the sample with primary

school has a significant negative impact on the wife's

employment participation; while the education level

of the sample of college, undergraduate and postgrad-

uate students has a significant positive impact on the

wife's employment participation, and the higher the

husband's education level, the greater the impact on

the wife's employment participation.

For the remaining control variables, the husband's

monthly working time has a significant positive im-

pact on the wife's employment participation.How-

ever, the increase of the husband's age will have a sig-

nificant negative impact on the wife's employment

participation. The ownership of the husband’s work

also has a negative impact on the wife’s employment

participation. Finally, the husband's nationality and

household registration have no significant effect on

the wife's employment participation.

3.2 A Test of the Effect of Husband's

Education Level on Husband's

GMI

Apply multiple regression analysis to test the signifi-

cance of the regression coefficients of the model (2).

The regression results are as follows (Table 3):

Table 3. Multiple regression results of model (2)

Explanatory Variable Coefficient Standard Error

Explanatory

Variable

Coefficient Standard Error

Primary School

0.151

（1.562）

0.097 Age

-0.024***

（-12.592）

0.002

Junior high school

0.557***

（5.750）

0.097 Nationality

0.486***

（6.705）

0.073

High school

0.786***

（7.570）

0.104

Household

registration

0.395***

（7.722）

0.051

College

1.054***

（8.536）

0.123 Political status

0.042

（0.688）

0.061

Undergraduate

1.317***

（10.286）

0.128 Ownership

0.034

（0.568）

0.060

Postgraduate

1.629***

（7.302）

0.223 Constant

7.402***

（44.509）

0.166

Monthly working hours

0.001***

（3.827）

0.000 Adjust R

0.352

Note: The data in parentheses are t values, ***, **, * indicate significance at the 1%, 5%, and 10% confidence levels, respectively.

NMDME 2022 - The International Conference on New Media Development and Modernized Education

The above regression results show that, except for

primary school, the husband's education level has a

significant positive impact on the husband's GMI, and

the higher the education level, the greater the impact

on the GMI. For example, compared with the illiterate

sample, the sample with a high school education has

a 78.6% increase in GMI, while the sample with a col-

lege education, the value is 105.4%, and at the post-

graduate level, the value rises to 162.9%, it can be

seen that higher education level especially above

graduate level has a great impact on GMI.

For other control variables, the effect of monthly

working hours on GMI is positive, The effect of age

on GMI is negative. Compared with the minorities,

the GMI of the Han nationality is higher; the sample

income of the non-agricultural household registration

is higher than that of the agricultural household reg-

istration, which is consistent with the actual situation.

Finally, The effect of political status and ownership

on GMI is not significant.

3.3 A Test of the Effect of Husband's

Education Level and GMI on

Wife's Employment Participation

Use binary logistic regression to test the significance

of the regression coefficients of the model (3). The

regression results are as follows (Table 4):

Table 4. Logistic regression results of model (3)

Explanatory Variable Coefficient Standard Error Explanatory Variable Coefficient Standard Error

Primary school

-0.354

（2.630）

0.218 Monthly working hours

0.001**

（4.418）

0.001

Junior high school

-0.294

（1.781）

0.220 Age

-0.017***

（15.335）

0.004

High school

-0.092

（0.150）

0.238 Nationality

-0.197

（1.342）

0.170

College

0.586**

（4.019）

0.292

Household

registration

0.004

（0.001）

0.120

Undergraduate

0.853**

（7.520）

0.311 Political status

-0.183

（1.689）

0.141

Postgraduate

1.561**

（5.297）

0.678 Ownership

-0.299**

（4.604）

0.139

GMI log

-0.089*

（3.270）

0.049 Constant

1.962***

（13.747）

0.529

Hosmer and

Lemeshow test

0.895

Note: The data in parentheses are wals values, ***, **, * indicate significance at the 1%, 5%, and 10% confidence levels, respectively.

It can be seen from the above table that the P value

of the Hosmer and Lemeshow test is 0.895>0.05, in-

dicating that the model (3) has a good fit. The core

explanatory variable--the husband's education level,

has a significant positive impact on the wife's em-

ployment participation above the college, and the

higher the education level, the greater the impact,

which is consistent with the regression results of

model (1). The core explanatory variable logarithm of

husband's GMI has a significant negative impact on

wife’s employment participation.

For other control variables, both husband's age

and work ownership have significant negative effects

on wife’s employment participation; while household

registration and political status have no significant ef-

fects on wife’s employment participation.

To sum up, if the education level of the husband

is in three stages: college, undergraduate and post-

graduate, the regression results of the above three

models are: the explanated variable 𝑊𝑜𝑟𝑘 has a re-

gression on the explanatory variable 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛, and

the coefficient 𝛽



is significant; the mediator varia-

ble 𝑌 has a regression on the explanatory varia-

ble 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 , the coefficient 𝛽



is significant; the

explanated variable 𝑊𝑜𝑟𝑘 is regressed on the medi-

ating variable 𝑌 and the explanatory variable

𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛, and 𝛿 is significant, so according to the

sequential test method of the mediating effect, it can

be seen that the mediating effect of the husband’s

GMI exists, that is, the husband's education level can

affect the wife's employment participation through

the mediating variable of the husband's GMI, and the

Does Husbandâ

Zs Education Level Affect Wifeâ

Zs Employment Participation? Mediation Effect Test Based on CGSS

husband's education level has a significant positive

impact on the wife's employment participation.

4 CONCLUSIONS AND

SUGGESTIONS

Relying on big data and using Stata computer statisti-

cal software to complete the mediation effect test, we

can draw the following conclusions: only when the

husband's education level is above college, the GMI

of the mediating variable can positively affect the

wife's employment participation.

Based on the further discussion above, in order to

increase the wife's employment participation, the fol-

lowing suggestions are put forward:

Increase investment in education. In modern soci-

ety, the employment participation of wives is not only

related to their own level of education, but also has a

significant positive correlation with their husbands’

education level. Therefore, in order to improve the

employment participation of women, the fundamental

is to improve the education level of the whole people,

especially to increase the investment in education at

the college level and above. Although our country has

expanded its higher education enrollment since 2000,

compared with developed countries, the proportion of

people with higher education in our country is still

small, and even fewer have received postgraduate ed-

ucation. Relevant data from the official website of the

Ministry of Education shows that in 2021, the number

of ordinary undergraduates and college graduates was

8.265 million. There are 10.013 million enrollments

and 34.961 million students in school

(Ministry of Ed-

ucation, 2021), and even fewer postgraduate students.

However, the total number of people in our country

in 2021 is about 1.41 billion. It can be seen that our

country's higher education investment needs to be

further improved.

Destroy gender social norms. In addition to edu-

cational level, women's employment participation is

effected by a variety of factors. One of the most im-

portant points is the social norms of gender. The tra-

ditional gender social norm advocates that “men take

charge of the outside, and females take care of the in-

side”, When roles conflict with the work, based on the

theory of gender dominance, women should give up

the work and return to their families (Cheng, He,

2017). However, with the progress of society, The

concept of equality between men and women is grad-

ually recognized. In the workplace, the government,

society and enterprises should try their best to elimi-

nate the “glass ceiling” for women's career advance-

ment, break down barriers to women's professional ti-

tle promotion, and create a good working space for

women; in the family, it is necessary to advocate the

sharing of housework by husbands and wives, free

women's productivity from housework, etc., then pro-

mote women's employment participation, especially

married women.

This research was supported by a project of social

science research of Shandong Youth University of

Political Science (Grant No.: SJYBXM202212)

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