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Case Study
Migrant Workers’ Residential Choices and China’s
Urbanization Path: Evidence from Northeastern China
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Jinqi Jiang, Ph.D. 1; Zhenhua Wang, Ph.D. 2; Wanzhen Huang 3; and Xiaohan Wei 4
Abstract: The controversy over whether the path of prioritizing large cities sprawling or the path of prioritizing small cities development is
better for Chinese urbanization is unresolved. In northeastern China, extremely low fertility and increasing rural emigration are weakening the
population foundation for industrialization and urbanization. Therefore, the approach to urbanization is key to a regional economic revival.
Survey data from 2014 on 1,242 migrant workers in 6 cities in Liaoning Province were used to analyze residential choices and their
determinants to respond to the controversy. The results found that midsized or large cities and cities near the hometown were preferred
over small cities and cities far from the hometown; therefore, prioritizing large cities sprawling is more responsive to migrant workers’
choices in northeastern China. Gender, age, educational attainment, wages, employment quality, income satisfaction, length of employment,
urban health insurance, history of family migration, and family land size predicted the choices. DOI: 10.1061/(ASCE)UP.19435444.0000523. © 2019 American Society of Civil Engineers.
Author keywords: Residential choice; Migrant workers; Urbanization path; Northeastern China.
Introduction
Background and Research Goals
Two distinct approaches to development are used for China’s
urban expansion. Prioritizing large cities sprawling encourages expansion by forming megacity clusters, and prioritizing the development of small cities strictly controls large cities’ urban sprawl and
encourages the development of small cities and, particularly, towns.
In policy, the different urbanization patterns shape development
strategies. China’s central government has chosen the latter pattern
as its main approach to urbanization, but this policy has not garnered consistent support from research and application.
Theoretical studies are mixed regarding which pattern China
should adopt for urbanization. For example, economists who
embrace the free market approach have argued that prioritizing
large cities sprawling is necessary because China’s economy would
likely benefit much more from the economic agglomeration and
economies of scale of large cities and megacities (Lu 2016). On
the other hand, sociologists and conservative economists have contended that prioritizing the development of small cities is better because so-called metropolitan diseases, such as air pollution, traffic
1
Associate Professor, Dept. of Agricultural and Resources Economics,
College of Economics and Management, Shenyang Agricultural Univ.,
Shenyang, Liaoning 110866, PR China. Email: [email protected]
2
Assistant Professor, Dept. of Agricultural and Resources Economics,
College of Economics and Management, Shenyang Agricultural Univ.,
Shenyang, Liaoning 110866, PR China (corresponding author). Email:
[email protected]
3
Postgraduate Student, Dept. of Agricultural and Resources Economics,
College of Economics and Management, Shenyang Agricultural Univ.,
Shenyang, Liaoning 110866, PR China.
4
Postgraduate Student, Dept. of Agricultural and Resources Economics,
College of Economics and Management, Shenyang Agricultural Univ.,
Shenyang, Liaoning 110866, PR China.
Note. This manuscript was submitted on September 13, 2018; approved
on March 5, 2019; published online on August 19, 2019. Discussion period
open until January 19, 2020; separate discussions must be submitted for
individual papers. This paper is part of the Journal of Urban Planning
and Development, © ASCE, ISSN 0733-9488.
© ASCE
congestion, and excessive population, are serious problems in
many Chinese megacities, and rural and urban development is
extremely unbalanced (Wen 2017).
In the policy applications, huge regional socioeconomic differences suggest that China might need to adopt diverse modes of
urbanization rather than a single approach. In the eastern coastal
regions, such as the Beijing-Tianjin-Hebei region, the Yangtze
River Delta, and the Pearl River Delta, populations are highly concentrated and the capacities of the central megacities in these regions have reached their limit. That situation has accelerated the
urbanization of small satellite cities and towns to relieve the population concentration and strain placed on the services in the central
cities. However, in the vast and sparsely populated midwestern and
northeastern regions, it is more reasonable to promote population
agglomeration in central cities to transform them into megacity
clusters. Therefore, the extent to which China’s urbanization should
focus on megacity clustering or small city/town development needs
further study.
In northeastern China (including Liaoning Province, Jilin
Province, and Heilongjiang Province), the fertility rate is almost the
lowest in the nation, and the populations, particularly working-age
laborers, consistently emigrate from this region, which has weakened the population base for industrialization and urbanization.
Therefore, the key issue for local governments in this region regarding economic revival is whether they should encourage people to
live and work in the central cities, such as Shenyang, Changchun,
and Herbing, to support the cities’ economic vitality and leading
influences on the region’s economic growth, or encourage people
to settle in the small cities to sustain the county-level economies.
Although the theoretical debate on the best type of urbanization
is ongoing, new urbanization is applying the small-cities approach.
However, the low quality of urbanization in the midwest and
northeast has not significantly changed, and the semiurbanization
phenomenon has become very common among the small and
midsized cities in these regions. Some scholars have proposed
that the persistent problems might relate to the local governments’
lack of a clear understanding of the actual demands and residential
choices of migrant workers (Sun 2015). Therefore, to effectively
advance China’s urbanization, migrant workers’ residential choices
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and the factors that influence their choices should be investigated.
Some of the specific questions to answer are
1. Do migrant workers want to permanently settle in cities?
2. Do migrant workers want to settle in large cities, midsized cities,
or small cities?
3. Do migrant workers want to settle in cities far from their
hometowns or near their hometowns?
4. What factors influence migrant workers’ residential choices?
5. How do these factors relate to migrant workers’ chosen types of
residence?
Literature Review
Many previous studies have examined migrant workers’ residential
choices and their determinants. The study results provide useful
theoretical and policy insights into migrant workers’ willingness
to settle permanently and the understanding of the progress of
China’s urbanization. However, some aspects need further investigation. First, although migrant workers’ willingness to become
urban citizens or to settle in cities has been deeply analyzed,
the types of cities in which migrant workers prefer to reside
has received less attention (Huang and Zhang 2013; Zhang
2011). Just three studies (Sun 2015; Xia 2010; Ye and Qian
2016) have considered it, which is insufficient for scientific
policymaking.
Second, a large regional socioeconomic development gap likely
relates to migrant workers’ settlement behaviors across regions, implying that the urbanization approach should differ by region. However, all previous studies on migrant workers’ diverse residential
activities used survey data from southeastern China. Northeastern
China, a region important to China’s national economy, is different
from southeastern China in that the population and the proportion
of migrant workers are relatively less than in, for example, the
Yangtze River Delta and Pearl River Delta regions. Migrant workers in the northeastern region mainly come from local rural areas
and have relatively high agricultural production involvement (Yang
et al. 2015). Thus, the northeastern region should be studied as a
unique case.
Because studies of the settlement choices of migrant workers
in this region are rare, this study used 2014 survey data on migrant
workers in six cities to investigate residential choices and their
determinants. Using Liaoning Province as a typical region of
northeastern China, this study responded to the question of which
urbanization pattern would be best for northeastern China. The
remainder of the paper is structured as follows. The theoretical
framework that guided the analysis is presented next. The following section explains the sampling, data collection, variables,
and regression model, and then the analytical results are reported.
Conclusions and policy suggestions are provided in the last
section.
Theoretical Framework
In contrast to some countries’ immigrants’ simultaneous transformation of occupation and identity from rural to urban, the process
in China has two progressive phases: (1) the occupational change
from farmer to migrant worker, and (2) the identity transition from
migrant worker to citizen (Liu and Xu 2007). Migrant workers’
residential choices are mostly important to the second phase of this
transition. In theory, the push-pull model (Bagne 1969; Lee 1966),
the Harris–Todaro migration model (Harris and Todaro 1970), and
the New Economics of Labor Migration theory (Stark and Bloom
1985) are important explanations of rural-to-urban migration. In
previous studies, scholars usually integrated the findings of the
© ASCE
Harris–Todaro model with those of the New Economics of Labor
Migration model into the push-pull model and used this modified
push-pull model to analyze the residential choices of Chinese
migrant workers (Ye and Qian 2016).
This study followed that body of literature and adopted the
modified push-pull model to frame its analysis. The pushing and
pulling forces simultaneously might determine residential decisions; however, the potential power and influences of these two
forces independently might vary depending on city size, location,
and individual characteristics. In other words, migrant workers with
similar residential choices and rural characteristics face similar
pushing and pulling forces, whereas those with different residential
choices and rural characteristics experience different forces. Moreover, the study considered whether the forces’ influence on migrant
workers’ residential choices and their effects depended on aspects
of the external environment, such as family characteristics, as well
as the employment and individual characteristics of the migrant
workers.
According to the results of the previous studies, four types of
factors might influence migrant workers’ residential choices as
pushing or pulling forces. The first type was individual demographic characteristics and human capital, which indicate individual abilities and attributes. This type includes gender, age
(Xia 2010; Wang et al. 2010), marital status (Bai and He 2002;
Xia 2010), and educational attainment (Chen and Huang 2003;
Xia 2010; Luo 2012; Sun 2015). Additionally, health status might
influence migrant workers’ expectations regarding job tenure and
employment status (Zhang 2006), which, in turn, might influence
residential choices.
The second type of factor relates to employment status and
characteristics. Stable and high-quality employment is the premise
for and foundation on which migrant workers settle in cities, and
it is the most important type of push-pull factor. Previous studies
have examined work environment (Luo 2012), job stability (Qi
and Zhang 2012), employment quality (Wang 2013; Ye 2011),
work experience in the city (Wang et al. 2010), and wages (Xia
and Zhang 2011). Based on the studies’ findings, the present
study introduced variables such as wages, job quality, employment
stability, income satisfaction, work environment satisfaction,
and length of employment to indicate employment status and
characteristics.
The third group of factors measured migrant workers’ social
and labor security. The effects of social security on migrant
workers’ settlement behaviors have been explored in many studies
as institutional factors (Wang and Cai 2006; Ye 2011), and they
even have been found to be the key factor in residential choices.
However, as rural–urban integration advances, the influence of
social security is weakening. However, it still deserves attention,
and this study included the variables of urban medical insurances
and pension insurances to examine the effects of social security.
Furthermore, the extent of labor security in employment might
influence migrant workers’ expectations about the stability of employment in the city, which might, in turn, influence their residential choices. Therefore, a measure of labor security was included in
this study’s analysis.
The fourth type of factor was family socioeconomic characteristics. A migrant worker’s migration and settlement decisions are
usually based on the interests of the entire family; therefore, family
factors might influence the intensity of a push-pull effect and, then,
residential choices (Cai and Wang 2007; Liu and Su 2005; Wang
2006). Based on the results of previous studies, three aspects of the
family were included in the analysis: family financial status, history
of family migration, and family land size.
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Data and Methods of Analysis
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Data
The data used to address the study’s objectives were derived from
the migrant workers survey (MWS) in Liaoning Province conducted in January of 2014.
The survey data were obtained through a two-stage stratified
sampling process. In the first stage, six cities in Liaoning Province
were selected as survey sites. Shenyang was representative of large
cities; Fuxin and Jinzhou were typical midsized cities; and
Kaiyuan, Tai’an, and Changtu were considered typical small cities
based on geographical location, extent of economic development,
and population size. Then, using the industrial distribution of the
Monitoring Report of Migrant Workers (MRMW) (NBS 2013) and
the regional distribution of migrant workers in Liaoning Province,
the sample sizes by industry and city were determined. In the
MRMW, the employment of migrant workers in 2013 was concentrated in the manufacturing, construction, and service industries,
accounting for 31.4%, 22.2%, and 34.1%, respectively. Based on
the industries’ distributions, we mainly selected a sample from the
manufacturing, construction, and service industries with a ratio of
30∶25∶35. Based on the regional distribution of migrant workers in
Liaoning Province, we obtained samples from each large, midsized,
and small city using a 4∶2∶1 ratio. Altogether, 1,279 individuals
were surveyed. From that, we excluded cases with less than three
months’ employment in the city during the past year under the NBS
definition of migrant workers. Then we dropped outliers and cases
with missing values. The remaining 1,242 cases were analyzed.
i
i
i
i
ð1Þ
24.88
72.12
Results
Table 1. Sample characteristics (n ¼ 1,242)
© ASCE
Based on the theoretical framework, the following equation was
developed to estimate the residential choices of migrant workers:
X
X
X
X
PðY ¼ jÞ ¼ α þ
β i Xi þ
γ i Zi þ
δ i Qi þ
θi L i þ ε
8.29
20.69
28.66
24.96
17.39
Table 1 provides the descriptive statistics of the sample. The sample
was 54.51% male and 45.49% female. Those born during or before
the 1950s (age 55þ), 1960s (45–54 years), 1970s (35–44 years),
1980s (25–34 years), and 1990s (15–24 years) were 8.29%,
20.69%, 28.66%, 24.96%, and 17.39%, respectively. Categorized
by generation using the year 1980 as the cutoff point, 57.65% comprised the older generation (born before 1980 and 35 years or older)
and 42.35% comprised the new generation (born after 1980 and
15–34 years old). About three-quarters of the sample was married
Gender
Male
Female
Age group
55þ (born 1950s and before)
45–54 (born 1960s)
35–44 (born 1970s)
25–34 (born 1980s)
15–24 (born 1990s)
Marital status
Unmarried
Ever married
Immigration
Intracity
Intraprovince and intercity
Interprovince
City
Small city (including county towns)
Midsized city
Large city
Methods of Analysis
where P = probability that residential choice Y has value j.
First, the choice was indicated as the choice (desire or willingness) to settle in cities (urban areas) versus hometowns (rural
areas). Then, based on the choice of city size and geographical
proximity to the hometown, the choice indicator was categorized
as: (1) hometown (rural areas), (2) small cities (and towns), or
(3) midsized or large cities; and as (1) hometown (rural areas),
(2) cities near the hometown, or (3) cities far from the hometown.
To analyze the first variable, a binomial logistic regression was
used for estimation, because the variable is a discrete dichotomous
indicator.
Because the latter two variables offered discrete choices with
J (J > 2) alternatives, a multinomial logistic model, a conditional
logistic model, and a mixed logistic model were available for estimation. When the characteristics of alternatives do not influence
the choice or lack of data about the characteristics of the alternatives, a multinomial logistic model is often employed. However, in
this study, although the characteristics of the alternatives influence
the choice as well as the attributes of the individuals, a conditional
logistic model or mixed logistic model should be used, because the
data did not include characteristics of the alternatives of the residential choice. Eq. (1) only includes individual characteristics, and
we used the multinomial logistic regression model to estimate.
The symbols X, Z, Q, L in Eq. (1) were the explanatory variables representing the four types of factors (individual demographic
characteristics, employment status and characteristics, social and
labor security, and family socioeconomic characteristics) described
previously. Table 2 lists the variables’ definitions and descriptions.
Sample Characteristics
Variable
(75.12%). Most of the immigration was intraprovincial (83.90%):
about 57.65% was intracity, and about 26.25% was intercity, intraprovincial immigration. Only about 16.1% had migrated across
provincial borders. The distribution across city types was 28.82%
in small cities (including county towns), 32.85% in midsized cities,
and 38.33% in large cities.
In terms of the representativeness of this sample, we found it to
be consistent with the results of the 2013 MRMW by NBS and the
sample of Liaoning Province in the 2013 China Migrants Dynamic
Survey (CMDS) by the National Health Commission R.P. China.
In the CMDS, 57.13% of the sample was male and 42.87% was
female. In the MRMW, 53.4% comprised the older generation, and
46.6% comprised the younger generation, (these percentages were
49.3% and 50.7%, respectively, in the CMDS. Thus, the MWS
sample is demographically representative of migrant workers in
Liaoning Province.
Proportion (%)
54.51
45.49
57.65
26.25
16.10
28.82
32.85
38.33
Migrant Workers’ Residential Choices
Table 3 lists the residential choices of the respondents. Most of the
respondents chose the city. Overall, 68.52% preferred the city, and
only 31.48% preferred their hometowns. About 20.45% preferred
small cities, and 48.07% preferred midsized or large cities. Almost
70% of those who preferred a city preferred midsized or large cities,
05019012-3
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Table 2. Variable definitions and descriptions (n ¼ 1,242)
Variable
Definition
Mean
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Residential preference
Residential Choice 1
Residential Choice 2
Residential Choice 3
Willing to permanently settle in the city (0 = hometown, 1 = city)
City size (0 = hometown, 1 = small cities, 2 = large or midsized cities)
Geographical proximity (0 = hometown, 1 = cities near hometown, 2 = cities
far from hometown)
Individual demographic characteristics
Gender
1 = male, 2 = female
Age
Number of years
Marital status
0 = unmarried, 1 = ever married
Educational attainment
Number of years of formal education
Health status
Self-assessed health (1 = low to 4 = high)
Employment status and characteristics
Wages
Monthly wages (CNY)
Value of Standard International Occupation Prestige Scale (SIOPS)
Employment qualitya
(1 = low to 9 = high)
Income satisfaction
Self-assessed satisfaction (1 = low to 5 = high)
Work environment satisfaction
Self-assessed satisfaction (1 = low to 5 = high)
Employment stability
Total number of job turnover after emigration
Length of employment
Number of years employed in current city
Social and labor security
Urban pension insurances (1 = yes, 2 = no)
Urban pension insuranceb
Urban medical insurances (1= yes, 2 = no)
Urban health insurancec
Labor contract
Labor contract (1 = yes, 2 = no)
Family socioeconomic characteristics
Family land size
The area of cultivated land (SI)
Family migration
Residing with family members in the city (0 = no, 1 = yes)
Family financial status
Logarithm of family net income in the past year
Standard deviation
0.69
1.17
0.97
0.46
0.88
0.77
1.45
37.26
0.75
8.57
1.08
0.50
12.02
0.43
2.27
0.33
2,699.49
3.45
1,619.96
2.24
3.11
3.40
2.72
7.99
1.03
0.96
5.75
7.33
1.77
1.81
1.61
0.42
0.39
0.49
9,982,019.96
0.59
10.84
14,322,028.64
0.49
0.69
a
Employment quality was measured with the SIOPS, constructed by Jiang et al. (2014) and Yang et al. (2015).
Urban pension insurance includes urban social pension insurance and commercial pension insurance, but not the new rural social pension insurance.
c
Urban health insurance includes urban social health insurance and commercial health insurance, but not the new rural cooperative medical insurance.
b
Table 3. Residential choices (n ¼ 1,242)
Size (percentage)
Respondents
Entire sample
Within city choices
Older generation (age 35 or older) (n ¼ 716)
Within city choices
Younger generation (age 34 or younger) (n ¼ 526)
Within city choices
Hometown
Small
city
Midsized or
large city
Hometown
City near
hometown
City far from
hometown
31.48
—
39.66
—
20.34
—
20.45
29.85
19.13
31.71
22.24
27.92
48.07
70.15
41.20
68.29
57.41
72.08
31.48
—
39.66
—
20.34
—
40.5
59.11
38.83
64.35
42.78
53.70
28.02
40.89
21.51
35.65
36.88
46.30
suggesting that prioritizing megacities and large, sprawling cities is
relatively more responsive to the choices of migrant workers in
Liaoning Province. About 40.5% of the respondents preferred a
city near the hometown, whereas only about 28.02% preferred
cities far from their hometowns. Considering only those respondents who preferred to settle in cities, 59.11% preferred geographical
closeness to and 40.89% preferred geographical distance from the
hometown.
The older generation (born before 1980 and 35 years or older)
was compared with the younger generation (born in 1980 or later
and 15–34 years old). The older generation was more likely than
the younger generation to prefer and to want to return to the hometown (39.66% versus 20.34% in both cases). About 60.34% of the
older respondents preferred the city, compared with 79.66% of the
younger generation. The difference between the two groups was
nearly 20% points. However, of the older people who preferred
cities, about 68.29% preferred midsized or large cities, which,
although less, was similar to the 72.08% of the younger people
© ASCE
Geographical proximity (percentage)
who chose that option. This finding suggests that there were no
large intergenerational differences regarding city size choices.
As mentioned previously, geographical proximity to the hometown was preferred by a relatively large percentage of the older
generation (39.66% versus 20.34%). Of the older respondents
who preferred a city, 64.35% and 35.65% preferred to settle near
and far from the hometown, respectively. These percentages were
53.70% and 46.30%, respectively, among the younger respondents,
and the corresponding ratio was about 0.10. A comparison reveals
that the differences by age group in geographical choices were
larger than the differences by age group in choices for small versus
midsized or large cities.
Determinants of Residential Settlement Choices
Cities versus Rural Areas
Table 4 reports the logistic regression results of the analysis of
residential choice as rural or urban. The analysis was a stepwise
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Table 4. Hierarchical logistic regression results for rural versus urban residential choice (ref: rural residential choice) (n ¼ 1,242)
Variable
Model 4
Odds ratio
Standard error
0.6417a
−0.0349a
0.2671
0.0989a
0.2269
Individual demographic characteristics
0.3765b
0.4019a
−0.0441a
−0.0401a
0.26
0.3492c
0.0794b
0.0498
0.1323
0.1501
0.3390b
−0.0385a
−0.0901
0.044
0.174
1.404
0.962
0.914
1.045
1.190
0.1513
0.0080
0.2295
0.0321
0.2044
Wages
Employment quality
Income satisfaction
Work environment satisfaction
Employment stability
Length of employment
—
—
—
—
—
—
Employment status and characteristics
−0.0003a
−0.0003a
b
0.0755b
0.0765
−0.0674
−0.0704
0.1639b
0.1285
0.0046
0.0053
0.0312a
0.0340a
−0.0002a
0.0610a
−0.085
0.1263
0.0074
0.0221b
1.000
1.063
0.919
1.135
1.007
1.022
0.0001
0.0326
0.0780
0.0834
0.0115
0.0105
Urban pension insurance (no)
Urban health insurance (no)
Labor contract (no)
—
—
—
Social and labor security
—
−0.6282c
—
−0.3525
—
−0.0399
−0.5595c
−0.3342
−0.0682
0.571
0.716
0.934
0.3326
0.3532
0.1638
—
—
—
0.5539
0.0587
90.88a
Family socioeconomic characteristics
—
—
—
—
—
—
2.1665a
1.1106b
0.0993
0.1158
153.63a
179.16a
−0.0089c
0.5044a
0.1045
1.1488
0.991
1.656
1.110
3.154
0.1244
192.42a
0.0051
0.1680
0.1094
1.2985
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Gender (female)
Age
Marital status (ever married)
Educational attainment
Health status
Family land size
Family migration (yes)
Family financial status
Constant
Pseudo R2
Likelihood ratio χ2
Model 1
Model 2
Model 3
p < 0.01.
p < 0.10.
b
p < 0.05.
a
c
hierarchical approach using four models. The explanatory power
of the models significantly increased across the models as the
variables were progressively added, because the likelihood ratio
chi-squared value increased with each model and was statistically
significant (p < 0.01) in every model. This finding indicates that
the four types of variables selected to explain variation in residential choices were appropriate.
All the variables were tested together in Model 4. Female
migrant workers were more likely than the males to prefer a city,
and the younger respondents were more likely than the older
respondents to prefer a city. Among the employment status and
characteristics’ variables, wages negatively influenced (p < 0.01)
residential choices, and employment quality (p < 0.10) and the
number of years employed (p < 0.05) positively influenced residential choices. Based on the estimated coefficients, the respondents with relatively high wages preferred the hometown, but higher
employment quality and longer employment tenure increased the
likelihood of choosing to settle in the city. Results for the effects of
social security and labor security found that respondents without
urban pension insurance were relatively more likely to prefer settling in rural areas. Two of the family socioeconomic variables were
significant. Respondents with relatively large family land size were
more likely than their counterparts to prefer rural residence, and
respondents residing with a family member were relatively more
likely to prefer city residence.
City Size Choices
Table 5 presents the results of the multinomial logistic regression
on city size choices. The overall model’s likelihood chi-squared
test result was statistically significant (p < 0.01), indicating that
the four categories of variables together significantly explained
the difference in choices for city size in the sample. The significant
© ASCE
variables predicting a small city choice were gender, age, marital
status, wages, income satisfaction, and length of employment.
Urban pension insurance, urban health insurance, and history of
family migration were also significant. The significant predictors
of the midsized or large city choice were age, wages, employment
quality, income satisfaction, length of employment, urban health
insurance, family land size, and history of family migration.
Comparing the estimated results for the two options (small cities
versus midsized or large cities), age, wages, and history of family
migration were similar in that they were significant predictors and
the effects were in the same direction. Specifically, younger respondents, those with lower wages, and those residing with other
family members in the city were relatively more likely to prefer
cities. Second, income satisfaction, length of employment, and urban medical insurance were significant influences on both options,
but the directions of the effects were opposite. These results mean
that migrant workers with a high level of income satisfaction, a
short term of employment, and without urban medical insurance
preferred small cities, whereas those with a low level of income
satisfaction, a long term of employment, and urban medical insurance preferred midsized or large cities. Third, gender, marital
status, and urban pension insurance significantly influenced the
choice of small cities, but they were not statistically important
to the choice of midsized or large cities. On the other hand, employment quality and family land size were statistically significant
predictors of choosing midsized or large cities, but not of choosing small cities. Specifically, females, respondents who were or
had been married, and those with urban pension insurance were
more likely than their counterparts to prefer small cities. Respondents with high employment quality and those with less family land
were more likely than their counterparts to prefer midsized or large
cities.
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Table 5. Multinomial logistic regression results on city size choices (ref: hometown) (n ¼ 1,242)
Small city
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Variable
Individual demographic characteristics
Gender (female)
Age
Marital status (married)
Educational attainment
Health status
Employment status and characteristics
Wages
Employment quality
Income satisfaction
Work environment satisfaction
Employment stability
Length of employment
Social and labor security
Urban pension insurance (no)
Urban health insurance (no)
Labor contract (no)
Family socioeconomic characteristics
Family land size
Family migration (yes)
Family financial status
Constant
Pseudo R2
Likelihood ratio χ2
Midsized or large city
Coefficient
Relative risk ratio
Standard error
Coefficient
Relative risk ratio
Standard error
0.5016a
−0.0332b
0.6702c
0.0591
−0.1222
1.651
0.967
1.955
1.061
0.885
0.2010
0.0107
0.3288
0.0452
0.3060
0.2012
−0.0434b
−0.3406
0.0367
0.2613
1.223
0.957
0.711
1.037
1.299
0.1615
0.0088
0.2425
0.0343
0.2122
−0.0003b
0.0452
0.2778c
0.1591
−0.0072
−0.0416c
1.000
1.046
1.320
1.172
0.993
0.959
0.0001
0.0444
0.1093
0.1151
0.0223
0.0163
−0.0002b
0.0705c
−0.2335b
0.0960
0.0100
0.0445b
1.000
1.073
0.792
1.101
1.010
1.045
0.0001
0.0345
0.0839
0.0889
0.0121
0.0113
−1.1336b
0.9329c
−0.2445
0.322
2.542
0.783
0.3868
0.4276
0.2131
−0.0664
−1.1028b
0.0442
0.936
0.332
1.045
0.3892
0.4016
0.1783
0.0004
0.8984b
0.1682
−2.8354
1.000
2.456
1.183
0.059
0.0060
0.2372
0.1497
1.7977
−0.0159b
0.3060a
0.0892
1.9155
0.1557
402.22b
0.984
1.358
1.093
6.790
0.0061
0.1809
0.1170
1.3861
p < 0.10.
p < 0.01.
c
p < 0.05.
a
b
Geographical Proximity Choices
The results of the multinomial logistic regression on the residential
choice of the hometown and near versus far from the hometown
are listed in Table 6. The likelihood chi-squared test was statistically significant at p < 0.01, indicating that the model had strong
explanatory power. Gender, age, wages, and length of employment
were statistically significant, and the coefficients had the same sign
for both options (near versus far from the hometown). Specifically,
females, younger respondents, those with longer employment, and
those with relatively low wages were more likely than their counterparts to prefer cities, regardless of the cities’ geographical proximity to the hometown. Second, educational attainment, employment
quality, and history of family migration were only significant to
the choice of cities near the hometown. Specifically, respondents
with relatively high educational attainment, high employment
quality, and those residing with family members were more likely
to prefer cities near the hometown. Third, health status and satisfaction with income, the family’s land size, and family financial
status had significant influences on the choice of cities far from
the hometown. Specifically, those with relatively better health,
dissatisfaction with income, less family land, and relatively better
family finances were relatively more likely to choose cities far from
the hometown.
Conclusions and Policy Implications
Although the Chinese government has clearly chosen to take the
small city (particularly town) development approach and to limit
large cities’ growth as its main focus for new urbanization, controversy continues over whether the large cities or small cities approach
is better. Due to obvious regional socioeconomic differences in
China, the best path for urbanization should be explored.
© ASCE
In northeastern China, low fertility rates and the net emigration
of working-age populations have weakened the population foundations for urbanization at the regional level. In this context, this
study was premised on the residential choices of migrant workers,
because they are the major source of population increases in
China’s urbanization process. Survey data collected in Liaoning
Province in 2014 were used to analyze aspects of residential choice
and their determinants to answer the question of which approach
should be used to direct urbanization in northeastern China.
The main findings were as follows:
1. The respondents significantly preferred permanent residence in
cities as opposed to rural areas (68.52% versus 31.48%).
2. Midsized or large cities and cities near the hometown were preferred over small cities and cities far from the hometown.
Among the respondents who preferred to settle in cities, 29.85%
chose small cities and 70.15% chose midsized or large cities.
About 59.11% of the respondents preferred to settle in cities
near their hometowns, and 40.89% preferred to settle in the
cities far from their hometowns.
3. Gender, age, wages, length of employment, and a history of family migration were similarly influential on residential choices.
Their effects were almost invariant on size and proximity of
cities.
4. Marital status, high income satisfaction, urban pension insurances, and lack of urban health insurances predicted the
choice to settle in small cities, whereas high quality employment, low income satisfaction, urban health insurance, and less
family land predicted the choice to settle in midsized or large
cities.
5. Geographical proximity was important to residential choice in
the sample. Educational attainment and quality of employment
were positively related to the choice of cities near the hometown, and relatively good health, low income satisfaction, good
05019012-6
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Table 6. Multinomial logistic regression results on geographical proximity choices (ref: hometown) (n ¼ 1,242)
Cities near the hometown
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Variable
Individual demographic characteristics
Gender (female)
Age
Marital status (married)
Educational attainment
Health status
Employment status and characteristics
Wages
Employment quality
Income satisfaction
Work environment satisfaction
Employment stability
Length of employment
Social and labor security
Urban pension insurance (no)
Urban health insurance (no)
Labor contract (no)
Family socioeconomic characteristics
Family land size
Family migration (yes)
Family financial status
Constant
Pseudo R2
Likelihood ratio χ2
Cities far from the hometown
Coefficient
Relative risk ratio
Standard error
Coefficient
Relative risk ratio
Standard error
0.3060a
−0.0419b
0.2035
0.0812c
−0.0328
1.358
0.959
1.226
1.085
0.968
0.168
0.009
0.267
0.037
0.239
0.3136a
−0.0376b
−0.3659
−0.0103
0.3905a
1.368
0.963
0.694
0.990
1.478
0.178
0.010
0.260
0.038
0.228
−0.0004b
0.0648a
0.0417
0.102
−0.0027
0.0233c
1.000
1.067
1.043
1.107
0.997
1.024
0.000
0.036
0.088
0.094
0.015
0.012
−0.0001c
0.0571
−0.2372b
0.145
0.012
0.0224a
1.000
1.059
0.789
1.156
1.012
1.023
0.000
0.039
0.091
0.098
0.012
0.013
−0.5041
−0.1675
−0.1452
0.604
0.846
0.865
0.355
0.376
0.183
−0.5773
−0.601
−0.0021
0.561
0.548
0.998
0.398
0.411
0.196
−0.003
1.0043b
−0.0036
1.0886
0.997
2.730
0.996
2.970
0.006
0.197
0.123
1.459
0.982
0.874
1.321
0.584
0.007
0.197
0.130
1.537
−0.0179b
−0.1345
0.2783c
−0.5379
0.1246
336.02b
p < 0.10.
p < 0.01.
c
p < 0.05.
a
b
family financial status, and less family land significantly predicted the choice to settle in cities far from the hometown.
Considered together, this study’s findings strongly suggest that
giving priority to large, sprawling cities is the best urbanization
approach in northeastern China, because it is relatively more responsive to the choices of the region’s migrant workers. Although
the population of migrant workers in northeastern China is much
smaller than in the southeastern regions, and intraprovincial migration, as the major migration pattern in northeastern China, is very
different from that in southern regions, where interprovincial migration is the major pattern, the findings of this study lead to the
same conclusions as the findings by Sun (2015) and Ye and Qian
(2016), who identified that giving priority to large, sprawling cities
was the best urbanization approach for southeastern China.
Moreover, when the findings for city size choices are combined
with the findings for proximity choices, it is clear that the municipal
governments in northeastern China must not solely focus on the
urbanization of their central megacities (e.g., Shenyang City,
Dalian City, Changchun City, and Harbin City), but must also
attend to their midsized cities. The authors recommend that provincial governments consider selecting two or three proximate
midsized and large cities to develop into city clusters and then
encourage migrant workers to permanently settle there as citizens.
To improve policies that promote permanent settlement of migrant workers in midsized and large cities, we suggest that the
municipal governments in northeastern China focus first on industrialization within urbanization and then drive urban development
through the development of industries with inclusive employment
creation (Lu et al. 2012).
During the study, we deeply investigated the reasons that small
cities and county towns did not attract migrant workers for permanent settlement. We concluded that the main reason was a scarcity
of vigorous leading industries in the second- and third-tier sectors
© ASCE
and/or a lack of agglomeration of leading industries, which created
insufficient employment opportunities for migrant workers. Thus,
the first step should be to transform and upgrade the industrial
structures of the midsized and large cities to fit the region’s actual
resources, such as labor force and land. Assuming that effective
industrial competitiveness could be ensured, excessive capitalization of the local secondary and tertiary industries should be prevented, and the industrial structure should be prevented from
deviating from local resource endowment and comparative advantage. The second step should be that the abilities to absorb mediumskill and low-skill workers from the upstream and downstream
industries should be considered when selecting leading industries
for the region, because immigration from nearby agricultural sectors is expected to be the main fuel of urban population growth in
northeastern China. Then, regarding employment, governments in
northeastern China must also improve migrant workers’ urban
employment skills and enhance their urban employment quality.
The social security system should focus on integrating the urban
and rural public healthcare systems to increase the attractiveness of
settling in cities. In addition, because cultivated land resources are
relatively abundant in northeastern China, decreasing the pull force
of the land might promote migrant workers’ choices for settling in
cities. Possible counterforces might be applied in the form of
sizable rewards, subsidies, or urban housing compensations for
migrant worker families that permanently leave their rural land,
followed by programs to help them acclimate to urban lifestyles.
Acknowledgments
The authors are grateful for financial support from the National
Natural Science Foundation of China (71303161, 71503173), the
Social Science Foundation of Liaoning Province (L16BGL038),
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J. Urban Plann. Dev., 2019, 145(4): 05019012
J. Urban Plann. Dev.
the Program for Liaoning Excellent Talents in University
(WJQ2015026), and the Youth Project of the Philosophy and Social
Science Research, Ministry of Education of China (13YJC790057).
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References
Bagne, D. J. 1969. Principles of demography. New York: Wiley.
Bai, N., and Y. He. 2002. “Return or emigration? A research on the return
migration of migrants in Anhui province and Sichuan province.”
[In Chinese.] Sociol. Res. 17 (3): 64–78.
Cai, H., and J. Wang. 2007. “The research on migrant workers’ permanent
migration intention.” [In Chinese.] Sociol. Res. 22 (6): 86–113.
Chen, X., and Z. Huang. 2003. “Factor influencing labor migration to
cities in economic developed areas; the case of Zhejiang Province.”
[In Chinese.] Chin. Rural Econ. 18 (5): 33–39.
Harris, J. R., and M. P. Todaro. 1970. “Migration, unemployment and
development: A two-sector analysis.” Am. Econ. Rev. 60 (1): 126–142.
Huang, Q., and G. Zhang. 2013. “An investigation of the willingness to
settle in small and medium cities of new generation migrant workers.”
[In Chinese.] World Surv. Res. 21 (7): 29–33. https://doi.org/10.13778/j
.cnki.11-3705/c.2013.07.015.
Jiang, J., D. Qi, and G. Zhang. 2014. “Prior job and present job quality
and job mobility of migrant workers.” [In Chinese.] South China Rural
Area 21 (7): 29–33. https://doi.org/10.15879/j.cnki.cn44-1099/f.2014
.0047.
Lee, E. S. 1966. “A theory of migration.” Demography 3 (1): 47–57. https://
doi.org/10.2307/2060063.
Liu, C., and J. Xu. 2007. “A study on the second generation peasant
workers and their citizenization.” [In Chinese.] China Popul. Resour. Environ. 17 (1): 6–10. https://doi.org/10.1016/S1872-583X(07)60002-3.
Liu, H., and Q. Su. 2005. “An empirical analysis of rural women
labor’s intention of settling in cities; The case of Jiangsu Province.”
[In Chinese.] Chin. Rural Econ. 21 (9): 42–47.
Lu, M. 2016. Great country and big city. [In Chinese.] Shanghai: Shanghai
People’s Publishing House.
Lu, M., H. Gao, and S. Hiroshi. 2012. “On urban size and inclusive employment.” [In Chinese.] China Soc. Sci. 33 (10): 47–66.
Luo, E. 2012. “The impact of employ ability on the willingness to live in
urban areas of migrant workers: A case of Shanghai City.” [In Chinese.]
Urban Prob. 31 (7): 96–102. https://doi.org/10.13239/j.bjsshkxy.cswt
.2012.07.009.
NBS (National Bureau of Statistics of China). 2013. “The monitoring report of Chinese migrant workers in 2013.” [In Chinese.] Accessed May
12, 2014. http://www.stats.gov.cn/tjsj/zxfb/201405/t20140512_551585
.html.
© ASCE
Qi, D., and G. Zhang. 2012. “Occupational mobility of migrant workers
and the urban settlement willingness.” [In Chinese.] J. Agrotech. Econ.
31 (4): 44–51. https://doi.org/10.13246/j.cnki.jae.2012.04.006.
Stark, O., and D. E. Bloom. 1985. “The new economics of labor migration.”
Am. Econ. Rev. 75 (2): 173–178.
Sun, Z. 2015. “Settlement intention of migrant workers in big cities and
development path of China’s new-type urbanization.” [In Chinese.]
Popul. Res. 39 (5): 72–86.
Wang, D., and F. Cai. 2006. “Migration and poverty alleviation in China.”
[In Chinese.] China Labor Econ. 3 (3): 46–70.
Wang, G. 2006. “The macro model of rural-urban migration based on the
maximization of farmers’ net revenue.” [In Chinese.] Chin. J. Popul.
Sci. 20 (2): 48–57.
Wang, G., G. Chen, and X. Wei. 2010. “Study on the influencing factors
of rural-urban migrant workers’ willingness to be urban citizens in
Shanghai.” [In Chinese.] Popul. Dev. 16 (2): 2–11.
Wang, Y. 2013. “Settlement intention of rural migrants in Chinese cities:
Findings from a twelve-city migrant survey.” [In Chinese.] Popul. Res.
37 (4): 19–32.
Wen, T. 2017. “Rural construction is way to avoid economics crisis.”
[In Chinese.] Dev. Small Cities Towns 45 (3): 6–10.
Xia, X., and H. Zhang. 2011. “Study on citizenry willing of new generation
migrant workers and its influencing factors.” [In Chinese.] Northwest
Popul. J. 32 (2): 43–51. https://doi.org/10.15884/j.cnki.issn.1007
-0672.2011.02.003.
Xia, Y. 2010. “Migrant workers’ willingness of residential place and
its factors: A survey from Wenzhou City.” [In Chinese.] Chin. Rural
Econ. 26 (3): 35–44.
Yang, X., M. Li, and J. Jiang et al. 2015. Migrant workers’ employment and
the urbanization in Liaoning Province [In Chinese.] Beijing: Economic
Daily.
Ye, J., and W. Qian. 2016. “Migrant workers’ urbanization willing in
different scale cities and path-selected for new-type urbanization.”
[In Chinese.] Zhejiang Soc. Sci. 32 (5): 64–74. https://doi.org/10
.14167/j.zjss.2016.05.008.
Ye, P. 2011. “Residential preferences of migrant workers: An analysis of
the empirical survey data from seven provinces/districts.” [In Chinese.]
Society 31 (2): 153–169. https://doi.org//10.15992/j.cnki.31-1123/c
.2011.02.001.
Zhang, Y. 2011. “Migrant workers’ willing of hukou register and policy
choice of China urbanization.” [In Chinese.] Chin. J. Popul. Sci.
35 (2): 14–26.
Zhang, Z. 2006. “Settlement in city or return-migration: A life-cycle analysis of migrant workers’ decision.” [In Chinese.] Chin. Rural Econ.
22 (7): 21–29.
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J. Urban Plann. Dev., 2019, 145(4): 05019012
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