The Effects of Internet-Using on Farmer Employment Choice

and Income Differentiation:

An Empirical Study Based on the CGSS2017

Jie Liu

and Ti Wang

School of Government, Beijing Normal University, Beijing, Peoples Republic China

Keywords: Internet-Using, Income Differentiation, Employment Choice, Multiple Regression Analysis, Farmer.

Abstract: The purpose of this paper is to study the impact of Internet-using on farmers income differentiation. Based

on the data of Chinese General Social Survey in 2017, this study uses Ordinary Least Square multiple

regression and Stata16 software to analyze model and data. By comparing the income levels of farmers who

use the Internet and those who do not use the Internet, this paper indicates that Internet-using affects income

differentiation by influencing farmers employment choices. The Internet-using can help farmers choose

more suitable jobs by information and communication technologies so that increasing their income. In

addition, in the further segmentation of non-agricultural employment, this paper studies the impact of the

Internet, and finds that Internet using has a positive impact on entrepreneurial employment, but a negative

impact on migrant employment. The empirical results show that Internet using is helpful to promote the

income differentiation of farmers.

1 INTRODUCTION

①

It is an important task in the new era to improve

farmers lives, mobilize their enthusiasm for

production and achieve common prosperity. With

the continuous improvement of rural Internet

infrastructure construction, Internet technology has

become an important carrier of farmers' production

and life. Farmers can not only find job opportunities

and conduct interviews on the Internet, but also

engage in agricultural e-commerce. According to the

50th Statistical Report on the Development of

Internet in China, as of June 2022, the number of

Internet users in China is 1051 million, and the

Internet penetration rate is 74.4%, including 293

million in rural areas, and the Internet penetration

rate is 58.8%.

The continuous expansion of the

Internet industry derived from the Internet has

significantly increased the income of some residents

https://orcid.org/0000-0001-9317-009X.

https://orcid.org/0000-0001-6993-3528.

Data source: The China Internet Information Center. The

50th Statistical Report on the Development of the Internet

in China, http: / / www.cnnic.net.cn/n4/2022/0914/c88-

10226.html.

living in rural areas, but at the same time, it has also

exacerbated the income gap between different

farmers. By using Internet, some farmers obtain the

information about employment, agricultural product

marketing, land circulation and find suitable jobs,

realizing the effective allocation of rural resources.

However, the others can't effectively use the Internet

to increase their income, which are limited by

Internet access conditions, their own age, education

and other factors. Based on this, this study uses OLS

multiple regression and Stata16 software to explore

whether Internet using aggravates the income

differentiation of farmers and how Internet use leads

to income differentiation of farmers by influencing

their employment choices.

Liu, J. and Wang, T.

The Effects of Internet-Using on Farmer Employment Choice and Income Differentiation: An Empirical Study Based on the CGSS2017.

DOI: 10.5220/0012069200003624

In Proceedings of the 2nd International Conference on Public Management and Big Data Analysis (PMBDA 2022), pages 12-17

ISBN: 978-989-758-658-3

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

2 LITERATURE REVIEW AND

HYPOTHESES

2.1 Internet Using and Farmer Income

Differentiation

With the continuous improvement of broadband and

other network infrastructure, the accessibility of

Internet using has been greatly improved in rural

residents (Luo and Liu, 2022), Internet technology

has become an important factor in farmers

production and life. In Solo's economic growth

model, the production factors are mainly divided

into capital (K) and labor (L). Internet technology is

added to the model as "technology development

level (A)"(Zhu et al, 2022). In the rural economy,

the use of Internet technology can not only help the

farmer to quickly capture the trading information of

agriculture, reduce transaction costs due to

information asymmetry, and improve income

opportunities. At the same time, it can effectively

improve the information access efficiency of the

rural labor market, reduce the cost of finding jobs

(Ma et al, 2022), and improve the opportunities for

rural surplus labor to engage in non-agricultural

employment, thereby increasing income. However,

for some people who are not good at using Internet

technology, or even do not use Internet technology,

they have lost many opportunities and ways to

improve their income because the less information

(Liu and Han, 2018), and have limited access to

Internet technology premiums (Zhang, 2022), so the

income gap between them and farmers who use

Internet technology is more growing. Based on this,

the first hypothesis is proposed. H1: Internet using

aggravates the income differentiation of farmers.

2.2 The Role of Employment Choice

The employment choice is closely related to the

income differentiation of farmers. For agriculture

farmers, they can timely obtain agricultural

information by using Internet, such as land

circulation and agricultural products trading, and

improve agricultural production efficiency. In

addition, "Internet plus" products, such as Internet

finance, can accelerate the digital transformation of

rural industries and reduce information transaction

costs, thus promote financial support for industrial

development (Li et al, 2021). For non-agricultural

farmers, the Internet is not only a tool that can

improve productivity, but also an important means

to expand social capital (Ma and Ning, 2017). By

using Internet, they can obtain tremendous labor

market information, broaden the scope of

employment and increase employment opportunities.

For some entrepreneurial farmers, the rural e-

commerce industry provides a platform for them to

start their own businesses, which benefits are greater

than employment (Song and He, 2021). Whether for

agricultural farmers, non-agricultural farmers or

entrepreneurial farmers, the use of Internet is not

significantly beneficial. Farmers of different ages,

educational backgrounds and family backgrounds

have different Internet using skills and information

identification capabilities. Farmers who with higher

education and are good at using Internet technology

often choose a suitable job; farmers who with low

educational background and are not good at using

Internet technology cannot choose useful

information or even indulge in Internet games, thus

aggravating the income gap of farmers. Based on

this, the second hypothesis is proposed. H2: Internet

using aggravates the income differentiation of

farmers by influencing their employment choices.

3 DATA AND METHODS

We use the OLS model and Stata16 software to

estimate the impact of Internet using on income

differentiation. The specific model is as follows:

11 2 1

Income nternet ontrolsIC

ββ

=+ + +

(1)

Secondly, in order to test the meditation effect of

employment choice, this paper refers to the

meditation test method of Wen et al (2004), and on

the basis of formula (1), are further added (2) and (3)

to build the meditation effect model:

21 2 2

Employment nternet ontrolsIC

γγ

=+ + +

(2)

31 2

Income nternet Employment

ontrols

αδ δ

δε

=+ + +

(3)

Above (1) ~ (3), controls including gender, age,

education, health, political status, marital status,

social security, family size, region,

is constant,

βγ

、、

are variable regression coefficients,

the error item.

The Effects of Internet-Using on Farmer Employment Choice and Income Differentiation: An Empirical Study Based on the CGSS2017

Table 1: Descriptive statistics of the variable.

Variable name Variable assignment Mean Standard deviation

Annual gross

income

Personal total income for last year (2016) (log

value

)

4.207 0.539

Annual labor

income

Occupational / labor income for last year (2016)

(

value

)

4.219 0.531

Internet using

Whether to use the Internet (ever used =1, not used

=0)

0.580 0.494

Employment

choices

Non-agriculture employment =1, farming or

unem

ed =0

0.480 0.500

Employment types Entrepreneurial =1, migrant =2, other =0 1.825 0.380

gender Male =1, female =0 0.530 0.499

age Actual age (using 2017 minus birth year) 45.950 12.255

Education level

No education =1, primary school =2, middle school

=3, high school=4, junior college or above =5

0.550 1.407

Health condition

Very unhealthy =1, compared unhealthy =2,

generally =3, compared healthy =4, very healthy =5

3.530 1.106

Political status

Communist Party member =1, non-Communist

Party member =0

0.910 0.283

Married Married =1, unmarried, divorced, or widowed =0 0.830 0.372

Social security

The following insurance conditions add: endowment

insurance or medical insurance, participation =1, not

articipation =0

1.750 0.752

Family size Number of family members 3.090 1.559

Region Region: East =1, central =2, west =3, northeast =4 2.210 1.018

We use the Chinese General Social Survey 2017

(CGSS 2017) data to investigate the possible impact

of income differentiation on internet using. The

survey adopts multi-stage stratified probability

sampling design, consisting of 28 provinces regions

nationwide. The CGSS data particularly suits the

analytical needs of this study because it includes

variables on income, working and so on. According

to the research needs, we retained the household

registration status of "agricultural registration and

residence household registration (formerly

agricultural registration)" and the age of 18 to 65

samples; while excluding in the current school stage,

obtained through the entrance to non-agricultural

registration and related variable information

seriously missing samples, leaving in 4527 samples.

Income differentiation, the key dependent

variable, is measured by annual total income and

annual labor income. Individual annual income is

measured by "individual total income of the whole

year last year (2016)", and annual labor income is

measured by "individual occupational/labor income

of the whole year last year (2016)". In order to

prevent the results due by the heteroscedastic

problem (Ma et al, 2022), the paper takes the log

value of income. The key independent variable,

internet using, is measured by the questionnaire

"Your use of Internet in the past year" According to

the existing research, we use the binary variables to

measure the Internet using.

①

The mediation variable

is employment choice, which is divided into three

forms: non-agricultural employment, farming and

unemployed. According to the item of "your current

work experience", engaged in non-agricultural

employment is assigned value of 1, and the farming

or unemployed individuals is 0. In addition, non-

agricultural employment is further divided into two

forms: employer or self-employment and employee.

Usually, the former is considered as entrepreneurial

employment, while the latter is considered as

migrant employment.

Considering that farmers employment choice and

income differentiation are also affected by other

factors, we add some control variables referring to

previous studies (Zhu et al, 2022; He et al, 2022),

including gender, age, education level, health

condition, political status, married, social security,

family size and other variables. In addition,

considering the possible differentiation in Internet

using among farmers in different regions, we were

introduced into the regional virtual variables,

divided into eastern, central, western and northeast

regions. Specific description of each variable and the

descriptive statistical results are shown in Table 1.

"Never use the Internet" is assigned value of 0, as well as

"few", "sometimes", "often" and "frequent" use the

Internet are assigned value of 1.

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4 EMPIRICAL RESULTS

4.1 Benchmark Regression

We use Stata16 software to analyze data and test

model. In table 2, model (4) and (8) report the

estimated results of the impact of Internet using on

farmers income differentiation. The regression

coefficient for the effect of Internet using on

individual total income was 0.298, and the

regression coefficient for the impact of labor income

was 0.288. The regression results show that the

Internet using has a positive impact on farmers

income, and is significant at the statistical level of

1%, which shows that the use of Internet is helpful

to improve farmers income. However, the

differentiation in the effect of the two incomes is

small. The reason for that the study sample is mainly

the farmer labor force, and their total income mainly

include the labor income. In addition, education

level, health conditions and social security have a

significant positive impact on farmers income.

Which indicates that individuals have more

opportunities and ways to obtain income and are

more likely to increase their income with the higher

education level, the better health and involved in

social security. The coefficient of gender and

married was also significantly positive, indicating

that married individuals had higher income than

unmarried, and male individuals earn more than

females. However, the coefficient of the influence of

age, political status, family size and regional

differentiation on farmers income is negative. With

the increase of age, the decrease of farmers income.

Compared with the central and western regions,

farmers in the eastern economically developed

regions have more advantages in increasing income.

The above empirical results test hypothesis 1.

4.2 The Meditation Effect of

Employment Choice

The impact of Internet using on farmers income

differentiation has been tested, so what mechanism

does Internet using affect farmers income

differentiation? In table 2, model (1) shows that

Internet using has a significant positive impact on

employment choice, indicating that Internet using

can promote non-farm employment. Secondly,

further add employment choice in Internet using

impact on income model, in table 2, model (5)

shows that the influence of the Internet using on the

farmers income still has a significant positive impact

(coefficient from 0.298 to 0.222), and the

employment choice has a significant positive impact

on farmers income. According to the meditation

effect of testing ways, employment choice plays a

partial meditation role in the relationship between

Internet using and farmers income differentiation,

and the meditation effect is 7.61%.

In order to make a more comprehensive analysis,

we divide the non-agricultural employment into

entrepreneurial and migrant employment. In table2,

model (2) and (3) show that Internet using has a

positive impact on entrepreneurial and a negative

effect on migrant employment. The possible reason

is that entrepreneurial farmers have a higher demand

for the Internet. With the development of

agricultural technology and e-commerce, commodity

production and sales need Internet. Thus Internet

may encourage farmers to find more opportunities to

entrepreneurship. On the contrary, migrant work has

low demand for Internet so that inhibiting the choice

of migrant employment. Secondly, in the table 2,

model (6) and (7) show that the Internet using still

has significant positive impact on income. And

entrepreneurial has a significant positive impact on

income but migrant employment has significant

negative impact on income. Above, it can be seen

that entrepreneurial and migrant employment play a

meditation role in the relationship between Internet

using and income differentiation.

①

The meditation

effect is respectively 0.91% and 0.95%. Based on

the above, hypothesis 2 supported.

In addition, the results with the annual labor income as

the dependent variable are consistent with the above

conclusions, and the specific results are not listed due to

the extent limited.

The Effects of Internet-Using on Farmer Employment Choice and Income Differentiation: An Empirical Study Based on the CGSS2017

Table 2: Mechanistic analysis of the impact of Internet using on farmers income differentiation.

variable

EC EE ME Annual gross income

Annual

labor

income

(1) (3) (5) (4) (2) (4) (6) (8)

Internet

using

0.234***

(0.016)

0.088**

(0.028)

-0.106***

(0.029)

0.298***

(0.017)

0.222***

(0.016)

0.138***

(0.021)

0.138***

(0.021)

0.288***

(0.017)

0.325***

(0.015)

0.104***

(0.016)

-0.090***

(0.016)

Gender

0.090***

(0.013)

-0.003

(0.020)

0.013

(0.020)

0.199***

(0.013)

0.170***

(0.013)

0.144***

(0.015)

0.144***

(0.015)

0.214***

(0.013)

Age

0.008***

(0.001)

0.001

(0.001)

-0.002*

(0.001)

-0.007***

(0.001)

0.004***

(0.001)

-0.004***

(0.001)

-0.004***

(0.001)

-0.008***

(0.001)

Education

level

0.028***

(0.005)

-0.020**

(0.006)

0.014*

(0.006)

0.040***

(0.005)

0.031***

(0.005)

0.037***

(0.004)

0.036***

(0.004)

0.036***

(0.005)

Health

condition

0.056***

(0.006)

-0.007

(0.011)

0.007

(0.011)

0.082***

(0.006)

0.063***

(0.006)

0.044***

(0.008)

0.044***

(0.008)

0.077***

(0.007)

Political

status

-0.065*

(0.023)

0.085**

(0.030)

-0.062*

(0.031)

-0.054*

(0.024)

-0.033

(0.023)

-0.033

(0.023)

-0.029

(0.023)

-0.058*

(0.025)

Married

0.025

(0.018)

0.118***

(0.028)

-0.114***

(0.029)

0.122***

(0.019)

0.114***

(0.018)

0.084***

(0.021)

0.086***

(0.021)

0.117***

(0.02)

Social

security

0.004

(0.009)

0.016

(0.012)

-0.016

(0.012)

0.057***

(0.009)

0.056***

(0.008)

0.069***

(0.009)

0.069***

(0.009)

0.058***

(0.009)

Family

size

-0.002

(0.004)

-0.001

(0.006)

-0.001

(0.007)

-0.014**

(0.004)

-0.013**

(0.004)

-0.015**

(0.005)

-0.015**

(0.005)

-0.018***

(0.004)

Region

0.093***

(

0.006

)

0.045***

(0.01)

-0.056***

(0.010)

-0.086***

(0.007)

0.056***

(

0.006

)

-0.080***

(0.007)

-0.080***

(0.007)

-0.085***

(0.007)

Constant

0.717***

(0.060)

-0.057

(0.088)

1.117***

(0.090)

4.029***

(0.062)

3.797***

(0.059)

4.270***

(0.066)

4.364***

(0.068)

4.110***

(0.064)

0.313 0.043 0.044 0.371 0.433 0.259 0.255 0.378

Note: EC=Employment Choice, EE= Entrepreneurial Employment, ME= Migrant Employment;

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

4.3 Robustness Test

To ensure the robustness of the estimated results, we

replace variables to test. First, the regression takes

the information source and Internet using frequency

as independent variables. The information source is

measured "Is the main source of information the

Internet (including mobile Internet)", and Internet

using frequency treated as 1-5. In table 3, models (1)

and (2) show that the Internet as the main source of

information significantly increases the farmers and

Internet using frequency will increase the income.

Secondly, the logarithm of family annual income

was used as the dependent variable. In table 3,

model (5) shows that the coefficient of Internet

using is still positive, that is, Internet using improves

family annual income. In addition, select the youth

subsample to analysis

①

. In table 3, models (3) and

(4) show that Internet using improves the income

level of young farmers. Thus, the above results have

strong robustness.

According to the UN WHO age classification criteria in

2013, the sample who was under 44 years was defined as

youth.

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Table 3: Effect of the effect of table replacement variables on farmers income differentiation.

variable

Annual gross income

Family annual

income

(1) (2) (3) (4) (5)

Internet using 0.294***(0.032)

0.208***

(

0.027

)

Information

sources

0.057**(0.023)

Frequency of

Internet using

0.092***(0.005)

0.085***(0

.008)

Control variable Control Control Control Control Control

Content 4.463***(0.060) 3.901***(0.065) 3.979***(0.090) 3.833***(0.093)

4.651***

(0.099)

0.324 0.370 0.302 0.313 0.168

Note: ***

<0.01, **

<0.05, and *

<0.1

5 CONCLUSIONS

This paper uses OLS multiple regression and Stata16

software to analyze data in order to examine the

Internet using impact on farmers income

differentiation. The empirical results show that:

Internet using promote the farmers income

differentiation. By using the Internet, the income

level of farmers significantly increased by 0.298

than not use the Internet. Second, Internet using

promotes farmers non-agricultural employment to

increase their income such as information and

communication technologies, and employment

choice plays a meditation role in the process. In the

further subdivision of non-agricultural employment,

Internet using positively affects entrepreneurial such

as providing technical support for entrepreneurship,

but has a negative impact on migrant employment.

Increasing farmers income is the focus of

"agriculture, rural areas and farmers" in China and to

achieve common prosperity. The popularization of

the Internet has provided more opportunities to

increase farmers income, but there are still a

considerable number of farmers who cannot enjoy

the digital dividend due to their own conditions.

Therefore, the government should improve the

construction and upgrading of rural network

infrastructure, strengthen the information skills

training of farmers, so that more low-income

farmers can participate in the digital economy.

Meanwhile, promoting non-agricultural employment

is an important way to increase farmers income,

narrow the gap between urban and rural areas, and

accelerate the process of urbanization. We should

promote the linkage between digital technology and

the factor market, provide services for farmers to

participate in non-agricultural employment through

the Internet, and promote the continuous increase of

farmers income.

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The Effects of Internet-Using on Farmer Employment Choice and Income Differentiation: An Empirical Study Based on the CGSS2017