How Hukou Affects Precarious Employment: A Quantitative Analysis
of the 2017 Chinese General Social Survey
Tengyi Wang
The Experimental High School Attached to BNU, Beijing, 100032, China
Keywords: Precarious Employment, Hukou, Binary Logistic Regression Model.
Abstract: Technological advancement and economic globalization have made jobs increasingly precarious worldwide,
including in China. Those precarious workers often suffer from employment insecurity, income inadequacy,
and lack of protection. According to the 2021 National Bureau of Statistics, 200 million jobs in the Chinese
labor market fall in the category of precarious employment, accounting for 22.2% of the total labor force. The
Chinese household registration system (hukou), one critical factor in the Chinese labor market, can play an
essential role in shaping precarious employment. However, less literature has quantitatively studied the
relationship between hukou and precarious employment. Based on the 2017 Chinese General Social Survey
(CCGS), this study divides “precariousness” into two indicators—employment status and part-time job
status—and uses binary logistic regression models to examine the association between hukou and precarious
employment. The results show that people holding agricultural hukou are more likely to be in precarious
employment. These findings suggest that policies on employment, compulsory education, and vocational
training should be gradually decoupled from the hukou status in order to break the rural-urban divide.
1 INTRODUCTION
With technological advancement and economic
globalization, jobs have become increasingly
precarious in China and worldwide. The term
“precarious employment” refers to irregular and
insecure work arrangements, including insufficient
salary, hazardous working conditions, low-income
security, and vulnerable labor relations (Kreshpaj et
al., 2020; Rönnblad et al., 2019). Precarious
employment was a distinguished feature in developed
and industrialized countries since the 19
th
century,
and in developing countries after World War II, the
prosperous economy and new legislation protecting
workers legal rights led to a new form of labor
market----the increase of precarious employment
(Tompa et al., 2007). In China alone, according to the
2021 National Bureau of Statistics
1
, there are a total
of 900 million labor force, among which the number
of precarious employment has reached 200 million,
accounting for a large proportion (Guo, 2021).
During the COVID-19 pandemic, the number of
1
http://www.stats.gov.cn/tjsj/sjjd/202201/t20220117_1826
479.html. Access on October 25, 2022.
precarious workers would continue to increase due to
the unstable and rapidly changing labor market
(Yueping et al., 2021). Most literature has examined
the characteristics and the consequences of
precarious employment in developed countries, such
as Germany, America, and Canada (Cranford et al.,
2003; Kalleberg & Vallas, 2018; Vosko, 2006). This
is because those developed countries have a long
history of precarious employment. Since the
industrialization era, they gradually built mature
legislations of the labor market and had mature labor
union systems, which were more advanced than
developing countries (Tompa et al., 2007). Thus, it is
worth studying precarious employment in China
because it is the largest developing country, which
hasn’t been studied thoroughly in this aspect.
This paper examines the characteristics of
precarious employment in the Chinese labor market,
and in particular, the association between household
registration status and precarious employment. The
household registration in China, named the hukou
system, is one major factor in unequal access to
employment in the labor market. In history, hukou
196
Wang, T.
How Hukou Affects Precarious Employment: A Quantitative Analysis of the 2017 Chinese General Social Survey.
DOI: 10.5220/0012072000003624
In Proceedings of the 2nd International Conference on Public Management and Big Data Analysis (PMBDA 2022), pages 196-204
ISBN: 978-989-758-658-3
Copyright
c
 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

was used to control population mobility, separating
workers between urban areas and rural areas. Even
though now the government gradually breaks down
such segregation by liberalizing the restriction that
hukou caused, the labor market divide is rooted in the
society. In particular, the hukou system created
deformed citizenship, which discouraged migrants
from moving, working, and truly integrating into
cities, finally causing them to work in an unstable
situation and to be precarious workers (Ngai, 2010).
So far, most studies on this topic took a qualitative
approach, thus it is useful to examine the association
through a quantitative analysis.
To explore these questions, we use the data from
2017 Chinese General Social Survey (CGSS). CGSS
is one commonly used national presentative survey in
social science research, and the 2017 wave is the most
recent one publicly available. Logistic regression
results show that people with agricultural hukou are
more likely to be precarious workers, who have
statistically significant lower wages and education
levels than people with non-agricultural hukou. These
findings suggest hukou not only causes the wage
discrimination which most of existing literature has
proposed but also leads to the unequal access to
precarious employment in the labor market.
2 LITERATURE REVIEW
2.1 The Characteristics and the
Consequence of Precarious
Employment
According to the International Labor Organization,
“precarious work is a means for employers to shift
risks and responsibilities onto workers. It is work
performed in the formal and informal economy and is
characterized by variable levels and degrees of
objective (legal status) and subjective (feeling)
characteristics of uncertainty and insecurity.”
However, as the development of labor markets
worldwide, the definition ILO proposed is too broad
to use, thus the academic area does not have a
common definition now. Hence, this study will
examine some core characteristics of precarious
employment first.
There are several definitions of precarious work in
the literature. The characteristics of precariousness in
the labor relation can be concluded in three
dimensions: employment insecurity, income
inadequacy, and lack of protection (Kreshpaj, 2020).
Burgess and Campbell (1998) used a comprehensive
perspective to define precariousness so that it can
assess all kinds of precarious employment, finding
that precariousness is featured in the discontinued,
unprotected job, which is excluded from standard-
employment benefits. Under the definition, the word
“precarious” infers casual, family work, fixed-term
contracts, self-employed, or temporary work.
Simultaneously, it described the condition of such
work, like insufficient salary, hazardous working
conditions, low-income security, and vulnerable labor
relations (Kreshpaj et al., 2020; Rönnblad et al., 2019).
In addition to the study above, lots of literature also
examines the descriptions of precarious employment.
Quinland (2012) demonstrated that the term
“precarious employment” is often used to describe
irregular and insecure work arrangements. Tompa
(2007) supposed that it should be defined as an
unstable, unprotected, insecure, and vulnerable
working status. In addition, Benach (2000) considered
precarious employment as temporary work or
insecure and informal jobs. Through all these
descriptions, we can conclude that precarious
employment is defined by some key features, such as
casual, family work, fixed-term contracts, self-
employed, or temporary work, poor working
conditions, and lack of legal or insurance protections.
Besides, based on the comprehensive
characteristics and features of precarious employment,
lots of literature studied the consequences of it. Some
research shows that there is a strong association
between precarious employment and unideal health
outcomes, including physical health and mental health.
It was found that precarious employment caused a
significant decline in workers” well-being and mental
health due to the unstable nature of the jobs (Gunn et
al., 2021; Rönnblad et al., 2019). Benach and
Muntaner (2007) believed that many characteristics
attached to precarious employment, like low
credentials, low salary, and identity of migrants, are
the main factors that cause workers’ adverse health
outcomes. In addition to the findings of the
relationship between health situations and precarious
employment, Oddo (2021) discovered that people
who have lower income or are female, racialized, or
less educated are more likely to be precarious workers
in America. The scoping review of Gray (2021) also
supported this conclusion and added that the identity
of migrant workers may cause workers to be
precarious and fall into the secondary labor market,
known as an insecure working situation, low income,
and lack of protection. However, according to Brady
and Biegert (2017), in Germany, variables
like demographic, education/skill, job/work
characteristics, and region cannot successfully explain
How Hukou Affects Precarious Employment: A Quantitative Analysis of the 2017 Chinese General Social Survey
197

the growth of precarious employment, so institutional
alternation looks like the most credible illustration.
2.2 Hukou and Precarious
Employment in China
The work of precarious employment in China has
been cumulating in recent years, and most of the
literature focus on labor relations, legal risks, social
security, and their impacts on workers’ well-being
(Benach et al., 2000; Liu et al., 2022; Ding, 2017; Liu,
2022; Du, 2020; Zhang, 2022; Qi et al., 2022; Wang,
2021; Zhao, 2022; Shao, 2022; Guo, 2021). For
example, Liu (2022) explored the identity of
precarious workers. She found that departed from
traditional “employer-employee” labor relations
precarious workers often have part-time jobs, weak
dependence, less monitoring, and a lower degree of
working sustainability. In addition, Shao (2022) did
comprehensive research on the tax risks of precarious
workers from the perspective of both workers and
companies. Discovering the lack of compliance with
tax rules, such as false invoicing, tax evasion, and
ambiguity taxing, Shao proposed some practical and
valuable suggestions on precarious employment, like
standardizing invoice mechanisms and building
external tax risk management. Like most of the
existing literature on precarious employment, Zhang
and Ding (2017; 2020) studied precarious workers’
willingness to join social security and old-age
insurance. They revealed that the mode of
participation and transfer and the incentive method are
the problems that restrict precarious employees from
taking part in insurance. Hence, they suggested
improving the accessibility of insurance policies and
fostering the propaganda of insurance. In conclusion,
precarious workers often suffer from an insecure
working situation, low income, and lack of protection,
known as the secondary labor market.
In China, in addition to the factors and
consequences above that influence precarious
employment, household registration status (hukou) is
a critical structural force shaping the labor market
outcomes, partly explaining the income inequality and
vocational separation in the labor market. Hukou is an
institution with the capacity to monitor and control
population migration and access to public services
(Wu & Zhang, 2014). Personal hukou for all Chinese
citizens is divided into two categories: hukou type and
hukou location. The hukou type is classified as
agricultural hukou or non-agricultural hukou, usually
referring to rural or urban hukou, respectively (Knight
et al., 1999). Another category is hukou location,
which means each person is also classified based on
where he or she registered for hukou.
So how does hukou influence the labor market?
Abundant literature shows that hukou discrimination
exists in the labor market, leading to differences in
income and vocations between people with
agricultural hukou and people with non-agricultural
hukou. On the one hand, hukou discrimination makes
it difficult for people with agricultural hukou to enter
specific sectors, industries, and occupations. On the
other hand, different departments and industries have
different requirements for human capital, which may
lead to the clustering of people with agricultural hukou
with low average human capital and urban workers
with relatively high human capital in different
departments, industries, and occupations (Xie, 2012).
Hence, by combining these two approaches, hukou
discrimination exists in the labor market, and people
with agricultural hukou have lower wages than non-
rural hukou holders. This result was also supported by
other research (Chen et al., 2016; Zhang & Wang,
2011). For example, Liu (2005), using data from a
Chinese household survey, discovered that people
obtaining urban hukou later in their lives are more
probably to be self-employed or unemployed than
people who born with urban hukou. Song (2014) also
pointed out that people with rural hukou face labor
discrimination in cities, including wage
discrimination, hiring discrimination, and pre-market
discrimination. Xu (2004), in his paper, suggested that
hukou discrimination in the labor market was caused
by political restriction, which was rooted in the
planned economy, path independence, education
segregation, and information asymmetry. Based on the
statistical examination, Xu proposed that human
resources cannot explain the income inequality
between people with agricultural hukou and people
with non-agricultural hukou. Therefore, consistent
with the analysis of the dual labor market, the hukou
segregation embodied in the labor market may be
caused by institutional factors, like the hukou type or
the current trend of the Chinese labor market. Ngai
(2010) did a case study on female labor in Shenzhen,
China, showing that the hukou system created
deformed citizenship, which discouraged migrants
from moving, working, and truly integrating into
cities, finally causing them to work in an unstable
situation and to be precarious workers. Mrs. Dong is
one of the examples. However, we need to note that
she failed to quantitatively examine the relationship
between hukou and precarious employment. All in all,
according to the analysis above, due to the
institutional effects, people with agricultural hukou
often end up in the secondary labor market, the same
as precarious workers.
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In light of the research mentioned above, it can be
seen that there is a lot of literature exploring the
impact of the hukou system on the Chinese labor
market, especially on job discrimination, labor-
market segmentation, internal labor migration, labor
welfare policy, labor mental health, and labor
integration (Benach et al., 2000; Dulleck et al., 2012;
Fields & Song, 2020; Guo & Iredale, 2004; T. Liu et
al., 2022; Meng, 2012; Rönnblad et al., 2019; Song,
2014). However, precarious employment, exploding
recently and now representing a vital part of urban
employment, has not been quantitively carefully
studied in the context of the hukou system. Hence, as
a new but important aspect of the labor market, the
relationship between precarious employment and
hukou is worth further exploration and discovery
2.3 Hypothesis
From abundant existing studies of the hukou system
and dual labor market, it can be concluded that
precarious jobs, which are featured in low income,
lack of protection, and dangerous working situations,
most likely appear in the secondary labor market.
Coincidentally, people with agricultural hukou also
usually end up secondary market, so they are likely to
be employed precariously and unstably. Therefore, I
proposed the hypothesis below:
Hypothesis: People with agricultural hukou are
more likely to be in precarious employment than
people with non-agricultural hukou.
3 DATA AND MEASURES
3.1 Data
Data were collected from the Chinese General Social
Survey (CGSS), the first nationwide, comprehensive,
and continuous large-scale social survey program in
China. By collecting data from various aspects of
Chinese society systematically, CGSS aims to
summarize the trend of social change, promote the
openness of domestic social science research, and
provide data for government decision-making
(link:http://www.cnsda.org/index.php?r=projects/vie
w&id=94525591). This paper uses the 2017 wave
because it is the most recent database of CGSS, which
was released on October 10, 2020. There are 12,582
valid samples that were completed in CGSS 2017 in
total, and the data published online contains 783
variables.
This study imposed several restrictions on the
analytical sample. First of all, the research object is
limited to a population sample aged 16-60 years
(N=7441). Second, the paper drops the cases that do
not report dependent variables and key independent
variables such as education status, annual labor
income, and working years. Finally, 6372 samples
were obtained in this research.
3.2 Measures
The main dependent variable of this paper is
precarious employment, including two dimensions,
employment status, and part-time job status. Each
dimension would measure whether the sample
belongs to precarious employment or not
(Precarious=1, Not Precarious=0). Following Kong
(2010), this study extracts the two most important
features or dimensions of precarious workers, which
are employment status and part-time job status.
Employment status demonstrates one’s labor identity,
like whether he or she is self-employed or family
worker or another form of labor. Part-time job status
indicates the “precariousness” or “informality” of a
worker by examining whether this worker has one or
several jobs simultaneously.
Employment status. I use the question “Which of
the following situations is more suitable for your
current work status?” to measure precarious
employment. Following Liu and Zhong (2005),
precarious employment (coded=1) is classified as self-
employed labor (with fewer than seven employees),
temporary employment, dispatched labor, family
workers, or freelancers. If the respondents answered
none of those categories, they would be coded 0.
Part-time job status. The second dimension of
precarious employment is part-time job status, which
means that precarious employment is confirmed if
they participate in several part-time jobs (Part-time
Status=1). To measure part-time job status, the
research uses the question, “What is the nature of your
present job?” If the respondents answered that they
work full time, they would be considered non-
precarious employees (Part-time Status=0). Otherwise,
if the respondents answered they worked part-time,
they would be considered precarious employees (Part-
time Status=1).
Hukou.
The independent variable is the type of
household registration (Hukou status), which is
divided into agricultural household registration
(Hukou=1) and non-agricultural household
registration (Hukou=0). Referring to other scholars’
analysis of respondents’ household registration in
CGSS, this study decided to use the item “What is
your current household registration status” to
distinguish the type of the respondent’s hukou.
How Hukou Affects Precarious Employment: A Quantitative Analysis of the 2017 Chinese General Social Survey
199

Control variables include gender (male=1,
female=0), age, education level, annual labor income
(LnInc), marital status (married=1, not married=0),
working years, and the square of working years (Yang,
2012). These are commonly used variables to examine
human resources (Xie, 2012). The annual labor
income is measured by the question, “What was your
personal occupation/labor income last year (2016)?”
and I take the logarithm as the variable value to make
it more normally distributed. Instead of using monthly
income as the variable, the study can avoid data errors
due to the unsteady income of precarious workers.
Therefore, it is likely to examine the correlation
between variables more accurately. Besides, the
education level of respondents is measured by the
question, “What is your highest education degree?”
The respondents chose from “never received any
education=1, literacy class=2, primary school=3,
junior high school=4, vocational high school=5,
senior high school=6, technical secondary school=7,
technical school=8, junior college (Adult higher
education)=9, junior college (Formal higher
education)=10, Undergraduate (Adult higher
education)=11, Undergraduate (Formal higher
education)=12, Graduate and above=13.” This study
makes the assumption that the education level
increases in the order above.
Table 1: Descriptive statistics.
Variables Mean Std.Dev. Min Max
Employment Status 0.26 0.438 0 1
Par
t
-time Status 0.10 0.296 0 1
Huko
u
0.65 0.477 0 1
Gende
r
0.47 0.499 0 1
Age 44.73 10.629 23 60
Annual Labor Income 46630.39 254513.127 0 9980000
LnInc 10.259 1.164 5.70 16.12
Education Level 6.04 3.391 1 13
Marital Status 0.94 0.246 0 1
Working Years 2.40 6.641 0 97
Working Years
2
49.87 215.035 0 9409
3.3 Analytical Strategy
The dependent variables, including employment
status and part-time job status, are binary categorical
variables (precarious=1, not precarious=0), and they
are influenced by k factors 𝑋

,𝑋

,𝑋

,…,𝑋

so the
binary logistic regression model was chosen to
explore the relationship between hukou type on
precarious employment. The model is presented
below:
𝐿𝑛


=𝛽

+𝛽

𝑋

+𝛽

𝑋

+⋯+𝛽

𝑋

(1)
In the model above, p represents the probability
of being precarious workers; 𝛽

,𝛽

,𝛽

,𝛽

represent
the regression coefficients. According to these, OR
(odds ratio) or exp(b) can be deduced:


=𝑒










⋯



(2)
Hence, the equation of p can be inferred:
𝑝=











⋯














⋯



(3)
By implementing the Hausman Test, the
significance is higher than 0.05, and the overall
accuracy of prediction in the crosstab is more than
70%, suggesting that the binary logistic regression
model fits very well.
4 RESULTS
4.1 Descriptive Analysis
Table 2: Descriptive Analysis between two different hukou
types.
Variables Agricultural
hukou
Non-agricultural
hukou
Employment
Status*
0.293 0.197
Part-time
Status*
0.116 0.062
Gende
r
0.463 0.477
Age* 43.88 45.19
Annual Labor
Income*
36207.23 65830.80
LnInc* 9.9565 10.7878
Education
Level*
4.82 8.29
Marital
Status
0.941 0.924
Working
Years*
2.11 2.94
Working
Years
2
*
37.48 72.93
*: p<0.05. t-test or chi-square test
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In this study, the hukou type was used as a
categorical variable to analyze other variables in a
crosstab. Because employment status, part-time job
status, gender, and marital status are categorical
variables, chi-square analysis was performed on them
in this study. According to the results of SPSS, the
significance of Pearson’s chi-square values of the
above four variables are 0.000, 0.000, 0.283, and
0.007, respectively. Hence, the marital status, part-
time status, and marital status were significantly
different between the two types of hukou (sig<0.05),
indicating that the two indexes of precarious
employment were significantly higher in the
agricultural hukou than in the non-agricultural hukou.
Besides, age, annual labor income, LnInc,
education level, working years, and the square of
working years are continuous variables, so they are
processed by t-tests to analyze the difference between
agricultural hukou and non-agricultural hukou. The
result shows that the significance of t-tests of those
six variables are all 0.000, indicating that they are all
significantly different in the two types of hukou.
Specifically, the age, annual labor income, education
level, working years, and the square of working years
in people with non-agricultural hukou are
significantly higher than in people with agricultural
hukou. Therefore, in light of the analysis above, the
indexes of precarious employment have a
significantly higher value in agricultural hukou
people, and the indexes of controlled variables
(human resources) are higher in non-agricultural
hukou.
4.2 Hukou And Employment Status
Then binary logistic regression model is used to
examine the hypothesis, controlling for gender, age,
LnInc, Education Level, Marital Status, Working
Years, and the square of Working Years. Results are
shown in Table 3.
Table 3: Employment Status
Model1 Model2
Hukou (agriculture=1) 0.063*** 0.161*
Gender (male=1) 0.509***
Age -0.008*
LnInc -0.184***
Education Level -0.162***
Marital status -0.048
Working Years 0.094***
Working Years
2
-0.003***
Note:
*
p < 0.05;
**
p < 0.01;
***
p < 0.001
Model 1 of Table 3 is a simple model that
examines the association between hukou type and
employment status. It shows that the association
between hukou and precarious employment is
positive and significant (b=.063, p<.001). Model 2 of
Table 3 includes not only hukou but also other control
variables which may affect the result, such as Gender,
Age, LnInc, Education Level, Marital Status,
Working Years, and the square of Working Years.
Importantly, the regression coefficient of hukou is
0.161, and therefore, it can be calculated that the odds
ratio, exp(b), equals 1.175. That is, the probability of
being precarious workers in people with agricultural
hukou is 1.175 times higher than that in people with
non-agricultural hukou. The results confirm/support
H1.
Most control variables are in the expected
direction. To be specific, male workers and younger
workers are more likely to be precarious workers
(b=.509, p<.001; b=-.008, p<.05). Education level is
negatively associated with the likelihood of being
precarious workers (b=-.162, p<001). Working years
have a significantly positive relationship with the
probability of being precarious workers, whereas the
square of working years has a significantly negative
relationship with it (b=.094, p<.001; b=-003, p<.001).
4.3 Hukou and Part-Time Job Status
Table 4: Part-time Job Status
Model1 Model2
Hukou 0.680*** 0.267*
Gende
r
0.505***
Age -0.010
LnInc -0.237***
Education
Level
-0.092***
Marital status -0.087
Workin
g
Years 0.166***
Working
Years
2
-0.005***
Note:
*
p < 0.05;
**
p < 0.01;
***
p < 0.001
How Hukou Affects Precarious Employment: A Quantitative Analysis of the 2017 Chinese General Social Survey
201

Table 4 examines the effect of the type of household
registration on the part-time job status of precarious
workers. Model 1 includes only hukou, and Model 2
adds all the control variables. Most of the other
variables fit the expectation from Table 1. People who
are male and low-education are more likely to do
part-time jobs. Particularly, Gender has a
significantly positive relationship with Part-time
Status (b=.505, p<.001), and Education Level has a
significantly negative relationship with Part-time
Status (b=-.092, p<.001). Besides, similar to the
results in Table 3, the regression coefficient of
working years is positive, and the regression
coefficients of LnInc, education level, and the square
of working years are significantly negative. , But Age
and Marital Status do not have a significant
relationship with the independent variable, which is
not as same as a result in Table 2.
Table 4 shows that the type of Hukou has a
significantly positive relationship with the likelihood
of being precarious workers; that is, people who have
agricultural household registration are more likely to
have several part-time jobs in the labor market.
Model 2 in Table 4 also shows that after controlling
for these variables, there is a significant positive
correlation between hukou and the dependent
variable, proving this association is statistically
significant. Being calculated from the coefficient of
0.267, exp(b) or the odds ratio is 1.306. That is, the
probability of being precarious workers in people
with agricultural hukou is 1.306 times higher than
that of people with non-agricultural hukou. These
results also support/confirm H1.
5 DISCUSSION AND
CONCLUSION
Using the data of 6372 observations from CGSS 2017,
this study applies a binary logistic regression model
to analyze the impact of hukou type (type of
household registration) on precarious employment.
The findings show a significantly positive
relationship between precarious employment and
agricultural hukou. More specifically, people with
agricultural hukou are more likely to be precarious
workers; chances are also high that people who have
agricultural hukou would work part-time, having an
unstable working situation. The association holds
even controlling for gender, age, education, and
working years, suggesting an enduring impact of the
hukou barrier in the Chinese labor market.
There are some limitations that must be
considered in this study. First, this study adopts cross-
sectional data from 2017. Although this is the most
recent, publicly available survey, the situation of
precarious employment could be worse during the
current COVID-19 pandemic. Therefore, if new
survey results are released, future studies can update
the database and pay more attention to the changes
that happened during the pandemic. Second, the
definition of precarious employment has other
dimensions that failed to be captured in this study due
to the limitation of the questionnaire of CGSS 2017.
Hence, other measurements, like contract status and
working situation, can be used in future studies in
order to have a better and more comprehensive
picture of precarious employment.
Some practical policy recommendations can be
drawn from this study. To begin with, noticing that
people with agricultural hukou are more possibly to
be precarious workers, the government needs to set
clear rules in the labor market to reduce
discrimination based on hukou status, which is the
same as employment discrimination based on gender.
However, the final source of this problem is the
unequal resources based on hukou status; that is,
people with agricultural hukou have fewer resources
than people with non-agricultural hukou, from
medical resources to educational resources, the most
important part of human resources. Therefore,
policies on employment, compulsory education, and
vocational training should be gradually decoupled
from the hukou status; instead, they should be based
on personal competitiveness.
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