OSIPP Discussion Paper : DP-2018-E-010

Why People Leave Their Rural Hometown:
Evidence from 8 Provinces in China

July 22, 2018
He Zhu†PhD. Student, Osaka School of International Public Policy(OSIPP)JEL Classification: J61
Keywords: Internal migration mobility, Decision making, Industry, Nested logistic
model
Abstract: This paper aim to clarify what motivate people to migrate from rural to urban
area in China. The focus of most previous studies of migration are restricted to the wage
gap between the origin and destination. However, this study uses the RUMiC (2008)
data set that has individual characteristics of migrants and stayers, combined with China
Statistical Yearbook data, to explore the decision making process on China’s rural tourban migration. This research provides empirical evidence that migration is a joint
decision-making process characterized by the choices of migration and destination. The
results also show that the living condition in hometowns pushes people to migrate. For
example, the probability of moving decreases by 25% if the consumption of the ruralarea increases by 20% ( 10,000 RMB).

†

Corresponding author. Osaka School of International Public Policy, Osaka University,
Address: 1-31 Machikaneyama, Toyonaka, Osaka 560-0043, JAPAN.
Email: zhuhesi@gmail.com

1 Introduction
Since the late 1979, China experienced the world’s largest migration flow from rural tourban areas according to the International Labor Organization (ILO). As a result, theurbanization of China grew from 18 percent at 1978 to 53 percent at 2015 according to
the World Bank.
Although concern for investment of human capital is a fundamental motivation
for the study of migration, most theoretical and empirical studies have been limitedly
focused on the wage gap. The human capital model — by far the base line of theory forthe paper on the decision making process of migration developed by Sjaastad (1962) —implies that the migrants’ goal is to maximize utility by choosing a location thatprovides the highest return to human capital, or labor supply. Estimates on the wage gap
that is defined as the differential between the wage from the origin and expected wage
from the destination) are often used to explain why people migrate.
Other studies on migration extend the human capital model by age and, network,
policy, among others (Zhao,1999 2003; Borjas, 1990; Clark et al., 2007). Consideringinternal migration in China, Zhao (1999) addressed that the data on wages in China’s
rural areas are highly ambiguous varialiable due to imprecise self-reporting andnon-pecuniary income. Furthermore, there exists an extreme situation in actuality where
people only have non-pecuniary wages in Chinese rural areas. This situation makes it
difficult to calculate the wage in the origin area. Therefore, directly using the wage gap
on rural to urban migration in China can be misleading.
In another side of view, previous migration studies, whether theoretical or
empirical, fail to address the alternative destinations, and are restricted to the hometown
and migrated destination. However, when people are generally considering migrating,there can be more than one alternative location. Hence, the influence and effects fromthe alternative location is lacking from the migration studies. A study done by Brown
and Moore (1970) discusses the location choice and suggests that the decision making
process of migration is a joint decision of migration and location choice, though they
did not provide any empirical results. A study on the urban relocation making process
was recently conducted by Lee and Waddell (2010). Their study provides empiricalevidence that the decision making process has two phases; one is to choose whether to
move, and the other is to choose where to relocate.
The starting in this chapter aims to analyze the decision making process ofrural to urban migration using China in two models. The first model explores migration
reasons besides the wage gap as there may be other reasons to make people leave theirrural hometown in China. The second model exams whether or not the decision to
migrate is a joint decision making process that is combined with the decision of
migration and decision of destination. In addition, the decision making process may also
be characterized as a hierarchical structure where the location choice may better be
described as the second-stage decision. This study uses the Chinese Longitudinal
Survey Data from eight provinces, and provides empirical evidence that the industrial
structure has a significant influence on people’s decision on migration. These findings
are based on the analysis on the migrants’ individual characteristics and the aggregate
data of each province from the China Statistical Yearbook provided by the National
Bureau of Statistics of the People’s Republic of China.
This paper is organized as follows. Section 2 provides a description of the
major policies affecting migration patterns in China. Section 3 reviews literature on
migration in China and countries outside China. A brief description of the data set isprovided in Section 4, and the empirical results are reported in Section 5. The lastsection discusses the implications of these findings.

2 Background
2.1 Hukou system
A policy known as urban Hukou (household registration system) was introduced before
1979. Based on this policy, employment, allocation of housing, food, and othernecessities were contingent on the registration system, which strictly restrictedrural-to-urban migration. This was the period when China recently survived from theGreat Chinese Famine. The original purpose of this policy was to control the populationof urban areas and to guarantee that there was an adequate population for agriculture.
However, the persistence of this restrictive policy had created severe inequality
regarding wage, education and, housing, among others.
In late 1979, the government began to loosen the hukou system andintroduced the household responsibility system (HRS) — a practice in China first
adopted in agricultural economy in 1979 and later extended to the other sectors of theeconomy — by which managers of local enterprises were held responsible for the
profits and losses they make. This system partially supplemented the egalitarian
distribution method, whereby the state assumed all profits and losses. This change madeit possible for people who were born in rural areas to purchase food or other necessities
for living without an urban residence. At the same time, China was on track toward
economic growth and welcomed large foreign investments in the manufacturingindustry in the Eastern urban areas. With the fruit of economic results in urban areas,increasing surplus of agriculture labor, and demand for cheap labor in the
manufacturing sector led to a rush of rural-to-urban migration.
Although the food shortage had been avoided, the government continues tomaintain the hukou system today. The restriction on migration from rural to urban areas
still has impacts on various aspect of people’s livelihood including employment, social
security and property rights. Since the power of the hukou system still exists in China,
this makes rural to urban migration in China different compared to that of othercountries.

2.2 Government’s effort on protecting the migrant worker
There are several documents issued by the Chinese government to support the living of
migrant workers outside their hometown provinces.
This included two policy documents issued in 2002 and 2003, named
Document Number 2 of 2002 and Document Number 1 of 2003, for the purpose of
initializing the elimination of labor market discrimination against migrant workers andthe legitimization of their working environment. In June 2006, the State Council passed
a series of measures to protect migrant workers’ labor rights with Circular No. 36. The
measures included these restrictions on minimum wages, solutions to wage defaults,
enforcement of labor contracts, and expansion of migrant workers’ social security
coverage.
However, these policies and the supporting system still were implementeddifferently across provinces, and thus, an investigation of China’s internal migration
given these policies will make a valuable contribution to this line of research.

3 Literature review
3.1 International migration
Migration is a topic that has been studied around the world for the past 50 years, whichis led by Australia, Canada and the United States. There are a variety of theoreticalmodels that have been developed to explain why people migrate. From the macro sideof view, migration has been explained as a result or as a process of economic
development (Lewis, 1954; Ranis and Fei, 1961; Harris and Todaro, 1970). At themacro level, people move from low-to-high wage areas to seek the premium from thewage differential. Compared with the macro-level models, the micro-level models focus
on individual choices (Sjaastad, 1962; Todaro, 1969, 1976, 1989; Borjas, 1990; Stark
1984). The micro-level models argue that individuals are rational, and act based on a
cost-benefit calculation, which motivates them to migrate. The literature based on thesemicro-level models support a view that individual characteristics influence people’s
choice of migration.
In recent years, there are some important findings in the field of migration.
Studies using the gravity modeled found the moving distance increase cost for
migration decision(Karemera et al., 2000; DA Plane, 1984; Flowerdew and Aitkin, 1982;
Cindy Fan, 2005). Studies based on “Push and pull” theory found that migration
decision making process is characterized by destination effect and condition effect(Dorigo and Tobler, 1983; D Hare, 1999; DS Kline, 2003; Fiona et al., 2016).

3.2 Migration in China
The subject of rural to urban migration in China has been extensively investigated, andfor the past years, many studies investigated why rural people migrate to urban areas.

Kevin Honglin Zhang (2003) used panel data to proved the existence of a causal
relationship between the growth rate of GDP and internal migration. A study conductedby Fan (2005) using cross-country data discovered a positive relationship between the
population size and people’s willingness to migrate. In another study, Fan (2005)
modeled China’s internal migration by the gravity model with population and distance.
In her latter study, she used GDP, population, and transportation distance as the
explanatory variables. Zhang and Song (2003) used the provincial data from the China
Statistical Yearbook to explain that economic development is caused by rural to urban
migration. Therefore, the above studies indicate that the GDP growth is typically being
used as factor of economic development to explain why people migrate. However,considering that the value of agricultural output is a sub-component of GDP, theresults may be unreliable. To deal with this problem, this paper uses the output ratio ofdifferent industries as the factor to explain why people migrate.
Two relevant studies were conducted by Zhao (1999) regarding the
motivation for migration. One discussed why rural people migrate to urban areas bydecision using data from the Sichuan province. This is considered to be the foundationof migration study in China, and her findings showed that young males tend to migrate.
The other study argued that people migrate form rural to urban areas as ahousehold-level decision making process.
Some other studies emerged that discussed why people migrate at theindividual level, most of which divide the destination into inter-province, intra-provinceand overseas. Most importantly, as far as the author has studied, none of them have everaddressed the alternatives of destination choice. To fill this gap, the second part of thisstudy focuses on the joint decision of migration with respect to migrating anddestination. Since this study focuses on the joint decision making process, theexplanatory variables do not only includ the decision of whether to migrate or not, butwill also include the decisions from several alternative destinations. Due to thelimitation of the data set, eight provinces were chosen for analysis in the migration
decision making process.

4 Data description
This study uses two data sets, namely the Longitudinal Survey on Rural Urban
Migration (RUMiC)in China from the Institute for the Study of Labor, IZA and the
China Statistical Yearbook from the National Bureau of statistics of the People’s
Republic of China.
The previous studies which used the IZA’s data set have focused on threedifferent aspects of migration. The first is the relationship between remittance andmigration. The second, wage gaps and the equilibrium of the labor market (Klaus et al.,2016; Rachel et al., 2015; Björn et al., 2014; Hartmut et al., 2013; Klaus et al., 2014;
Zhao and Qu 2014). The third, the well-being of children (Huang 2015; Meng andYamauchi 2015) and the left-behind elders (Sylvie and Wang 2016; Zhao et al., 2016;
Rachel et al., 2015; Klaus et al., 2015). However, none of them analyzed the decision
making process for people to migrate from rural to urban areas.
The RUMiC consists of three parts: The Urban Household Survey, the Rural
Household Survey, and the Migrant Household Survey. It was initiated by a group of
researchers at the Australian National University, the University of Queensland, and the
Beijing Normal University and was supported by the Institute for the Study of Labor(IZA), which provides the Scientific Use Files. The financial support for RUMiC wasobtained from the Australian Research Council, the Australian Agency for International
Development (AusAID), the Ford Foundation, IZA and the Chinese Foundation ofSocial Sciences. The IZA data is a face to face interview data temporal covered the yearof 2008. This study used the Urban Migrant Survey (UMS) and Rural Household
Survey (RHS) to cover the provinces of Anhui, Hubei, Sichuan, Chongqing, Shanghai,
Jiangsu, Zhejiang, Henan, and Guangdong.The sample size combined 31,791
individuals from RHS and 8,446 individuals from UMS, which totals 40,237 individuals.
The coverage areas contain the main migrant supplying provinces (Sichuan, Chongqing,
Anhui, Hubei, Henan), and the main recieving provinces (Guangdong, Zhejiang,
Jiangsu). The macro-level data set of each province was provided by the National
Bureau of statistics of the People’s Republic of China.
The main purpose of this study is to analyze the decision making process why people
leave their rural homes. The reason for restricting individuals between 10 to 75 years ofage rather than using the whole data set is that children and the elderly are unlikely to be
involved in the decisions of their family.
Table 2 presents the summary statistics for the variables used in the analysis. This
study covers eight provinces. The people in the eight provinces are considered to havepossibility to move to the other seven provinces. Table 3 gives the statistics of thedestination provinces (including non-migrants).

Table 1. Studied Samples
Freq.

Percent

Cum.

Stay

33,388

93.95

93.95

Move

2,150

6.05

100

Total

35,538100

Wave 2008
Source: 2008 survey data of RUMiC
Table 2. Destinations included in the analysis
Province

Freq.

Percent

Cum.

Jiangsu&Shanghai

6,487

18.26

24.69

Zhejiang

3,842

10.81

29.06

Anhui

4,291

12.07

41.14

Henan

4,542

12.78

53.92

Hubei

4,315

12.14

66.06

Guangdong

5,434

15.29

81.35

Chongqing

2,222

6.25

87.6

Sichuan

4,405

12.4

100

Total

35,538100

Wave 2008
Source: 2008 survey data of RUMiC

5 Methodology
5.1 Stay or move choice models
Migration can be triggered by income gaps between the rural and urban areas (Sjaastad
1962). The majority of the previous studies argue that the motivation of migration is
because of the wage gap, however, it is almost unrealistic to gain the real income ofrural individuals due to non-pecuniary income exist. In addition, the Urban Migration
Survey (UMS) 2008 reported that over 50 percent of the people chose to migrate
because they are “too poor at hometown” or they have “no future in their rural
hometown”. Thus, this study uses people’s living condition to explain why people
migrate from rural to urban areas. Thus, this study takes into account living condition
can be one motivation that caused people migrate from rural to urban areas.
This study defines migration as moving to another province, like the previous studies.
This study is consistent with recent studies in the first that it uses the economic factorsof migrants’ rural hometown and individual factors such as age, gender, marriage status,and educational level as the determinate factor of migration. Table 3 explains thestatistical description of the variable, and Table 5 shows the results of the logit model.
Table 6 provides the marginal effects of the model (3) regarding the possibility of
migration.
This study shows similar results with tape in the previous studies in a point thatyoung males tend to migrate than old females. However, this study also shows thatpoverty is another factor that pushes people to migrate. Considering Table 6, themarginal effects from the results imply that the percentage of moving decreases almost
more than 50% if the consumption of rural areas increases by 20%. Here, a 20%
increase in the consumption means that 10,000 RMB will be increased, when people inrural areas consume at an average of 50,000 RMB per year. Therefore, people are likely
to choose migrate rather than staying.

Table 3. The explanation of variables for stay or move model
Variables

Mean

Std. Dev.

Min

Max

Male
Age

0.53
37.04

0.50
15.88

0.00
10.00

1.00
74.00

MarriedNo school
Average rural living standard

0.64
0.056
0.48

0.48
0.23
0.21

0.00
0.00
0.32

1.00
1.00
1.13

Erural

0.16

0.18

0.02

0.73

3.69
14795.81

0.61
4034.672.60
11097.66

4.60
23622.38

Unemployment
Wage-gap
wave 2008

Source: Survey Data and China Statistical Yearbook in 2008

Table 4. Data description
Variables
Average rural living standard(million RMB)
EruralUnemployment
No school
Male
Age
MarriedWage-gap
wave 2008

Description
Consumption of rural areas divided by Population ofrural areas
Electricity consumption of rural areas divided by
Population of rural areas
unemployment rate of rural provinces
Never attended school
Gender for individuals (1 for male, 0 for female)Age for individuals
Married status for individuals (1 for married and didn't
get divorced, 0 for others)
Income of urban minus consumption of rural for
individuals (million)

Table 5. Results of logit model for the stay-or-move model
VARIABLES

(1)

Male

0.317***

0.314***(0.0457)(0.0845)-0.0555***-0.0644***(0.00239)(0.00463)

1.177***

0.746***(0.0663)(0.110)Age

Married(2)

No school(3)0.497*(0.2561)

Average rural living standard

Erural Per person

Unemployment-1.037**-18.33***(0.519)(0.806)-1.653**-2.088**(0.654)(1.016)0.241***

2.676***(0.0546)(0.109)Wage-gap

0.00122***(2.61e-05)

Constant-1.783***-2.991***-21.12***(0.0642)(0.196)(0.589)Observations
35,538
Note: Figures in parentheses are t statistics.
*** Significant at the 1% level** Significant at the 5% level* Significant at the 10% level
Source: author’s calculation

35,53830,021

Table 6. Marginal effect for the Logit model
Delta-method
[95%
dy/dx

Std. Err.

z

P>z

Conf.

Interval]

Male

0.005

0.001

3.91

0.000

0.002

0.007

Age

-0.0010.000-15.840.000-0.001-0.001Married

0.011

0.002

7.02

0.000

0.008

0.014

No school

0.007

0.004

1.94

0.053

-0.000

0.015

Average rural living standard-0.271

0.018-15.320.000-0.305-0.236

EruralPerperson-0.030

0.015

-2.00

0.045

-0.059-0.001

Unemployment

0.040

0.002

16.74

0.000

0.035

0.044

Wag-gap

0.000

0.000

20.21

0.000

0.000

0.000

Source: Survey Data and China Statistical Yearbook in 2008

5.2 Joint decision model with destination choice
In China, there are few studies regarding destination choice. Even though Shuming(2007) and Fan (2005) showed that moving distance(distance from the origin to thedestination) has influenced migration decision, although their studies did not
specifically consider the presence of alternative destinations. Brown and Moore (1970)
discussed location choices among alternative destinations. Their study decomposed
location choice into two phases: the first phase considers both external and internal
factors for the stay-or-move decision; and the second considers a choice of destinations.
This two-stage decision structure motivate the use of the hierarchical model to model
the migration decisions.

Thus, in this study, the migration choice is considered as a joint decision making
process by individuals. The nesting structure is assumed to include a binary mobility
choice of ‘stay’ or ‘migrate’ at the top level, and a multiple choice of location at thebottom level. The ‘stay’ nest stands for hometown (hukou province), and the ‘migrate’
nest includes the other seven provinces as alternative destinations.

Figure 1. The Structure of nested logit model
Mobility choice (m):M1= stay
M2= migrate

M1

M2

Location choice (l):𝑙1 = 𝑠𝑡𝑎𝑦
𝑙𝑗 = 𝑑𝑒𝑠𝑡𝑖𝑛𝑎𝑡𝑖𝑜𝑛 𝑝𝑟𝑜𝑣𝑖𝑛𝑐𝑒𝑠l1l2( j= 2,…8)l3….lj.…l8

the probability of an individual choosing location l in nested logit model(NLM) is
defined as,
𝑃(𝑙) = 𝑃(𝑙|𝑚) ∗ 𝑃(𝑚) (1),where, 𝑃(l|m) is the conditional probability of choosing location from the 𝑚th nest.
P(m) is the marginal probability of choosing 𝑚th nest. The bottom level conditional
probability is equivalent to the standard MNL equation and is written as,
𝑃(𝑙|𝑚) =𝑒 𝑉𝑙 𝜇𝑙∑𝑙′ ∈𝐿𝑚where,′𝑒 𝑉𝑙 𝜇𝑙(2),

𝑉𝑙 represents the observable components of the utility function for eachelemental eta alternative and 𝜇𝑙 is the corresponding scale parameter. The marginal
choice probability of choosing 𝑚th nest is,′𝑃(𝑚) =𝑒 𝑉𝑚 𝜇𝑚𝑉′

𝜇∑𝑚′ 𝜖𝑀 𝑒 𝑚′ 𝑚(3),

where, 𝑉𝑚′ is the logsum ,or the inclusive value, associated with the 𝑚th nest, and𝜇𝑚 is the top level scale parameter. The logsum represents the expected value of the
maximum within the random utilities of all the alternatives in the 𝑚th nest. For
random sampling, the expanded logsum can be written as,′
𝑉𝑚′ = (1⁄𝜇𝑙 ) ln {∑𝑙′ ∈𝐿𝑚 [(1⁄𝑅 ′ ) 𝑒 𝑉𝜇 𝜇𝑙 ]} (4),𝑙

In this study, the stay nest is the hukou province, 𝑙1, thus the 𝑢𝑚 is degenerated andrestricted as one location. By implementing the NLM as the methodology to analyze thedestination province characteristics, the attractiveness of destination 𝑙 for an individual𝑛 is expressed by the utility function,
𝑉𝑙 = 𝛼𝑋𝑙 + 𝛽(𝐷𝑛 ) + 𝛾𝐼𝑛 𝑋𝑙 ,where 𝑋𝑙 is the choice-specific explanatory variable containing an array ofcharacteristics describing alternative 𝑙, 𝐷𝑛 is the moving distance for each individual
as the interaction term, and 𝐼𝑛 is an array of attributes for individual 𝑛. This utility
function reveals that when an individual makes a rational decision of location, he or she
takes into consideration both characteristics of the potential alternative destinations andtheir own characteristics. 𝑦𝑚𝑛 is 1 if individual 𝑛 chooses nest 𝑚, and 0 otherwise.
And 𝑦𝑙𝑚𝑛 is 1 if individual 𝑛 chooses alternative 𝑙 in nest 𝑚, and 0 otherwise.
In this study, the NLM is estimated using the Survey of 2008. In the model, Shanghai
is included in the Jangsu province because the area of Shanghai is too small whencompared with other provinces. Another reason is that Shanghai is a city belonging to
Jiangsu before 1927. The explanatory variables for the NLM are as follows in Table 8
for each province. Because this study covers 8 out of the 34 provinces in China, it iswould be fair to admit that the result may be partially because of the movement bothwithin and outside of the 8 providences. Nevertheless, both the main receiving
provinces (Jiangsu, Zhejiang, Guangzhou) and the main sending provinces (Sichuan,
Anhui) are included in the data set, resulting in significant coefficient. The explanatory
variables are listed in Table 8 for both individual and province levels. Note that eachprovince means the hukou provinces of individuals before migration occurs. The
distance in this study stands for the geographic distance between individuals’ hukou
provinces and their destination provinces. The distance variable has been calculated by
the author according to Vincenty's (1975) equations (by assuming a sphere with a radius
of 6,371km to approximate the shape of the earth and computing great-circle distances).
The reason for using the industrial structure (the ratio of the secondary industry output
value and the ratio of the tertiary industry output value) as the explanatory variables in
the analysis is twofold. One reason is that the previous studies suggest economic factors
are the main factor for migration, and the second is that few migrants work in the
primary industry. Thus, I set up the following hypotheses: first, the ratio of thesecondary industry to GDP for each alternative location is the attracting factor forindividuals; second, the ratio of the tertiary industry to GDP for each alternative
location is the attractiveness factor for individuals; third, the moving distance for eachalternative location is the cost factor for individuals.

Table 7. Mobility matrix
Hukou Province
Henan
Hubei

Jiangsu

SichuanZhejiang

Anhui

Chongqing

GuangdongAnhui

83.46%

0.08%

0.05%

0.72%

0.17%

0.40%

0.48%

0.24%

Chongqing
Guangdong
Henan

0.00%
0.34%
0.95%

96.13%
0.88%
0.04%

0.00%
99.82%
0.00%

0.00%
2.32%
92.86%

0.06%
2.78%
0.14%

0.00%
0.09%
0.00%

2.93%
2.18%
0.04%

0.00%
0.13%
0.40%

Hubei
Jiangsu
SichuanZhejiang

0.11%
9.65%
0.05%
5.44%

0.42%
0.84%
0.88%
0.72%

0.05%
0.02%
0.04%
0.02%

0.82%
1.58%
0.06%
1.65%

95.03%
0.80%
0.10%
0.91%

0.05%
98.30%
0.05%
1.11%

0.21%
1.06%
92.40%
0.71%

0.11%
1.65%
0.00%
97.47%

Total

100.00%100.00%100.00%100.00%100.00%100.00%100.00%100.00%Destination

Source: 2008 Survey Data

The mobility matrix from the 2008 survey is shown in Table 7. The numbers
represent the percentage of individuals who chose a particular province among the eight
alternative destinations. The mobility matrix shows that Anhui, Henan, Hubei, andSichuan are the net sending provinces, whereas the net receiving provinces are
Guangdong, Jiangsu, and Zhejiang. Also, Chongqing is described as a province which isneutral in terms of sending and receiving province.
Table 8 presents the definition of the variables that are used in the Nested Logit
Model. This study uses the log moving distance of each individual to seven otheralternative destinations (location of potential destination) and industrial structure
measures in the destination (secondary and tertiary) as the choice specific explantory
variables. The results of the NLM are shown in Table 9. Table 10 summarizes theresults of Tables 8 and 9.

Table 8. Variables and descriptions in The destination choice model
VARIABLES

Description

Log-distance The logit of the geographic distance forchoiceeach individual to each alternatives
secondThe
for each alternative- ratio of secondary industry output value
choicetertiaryTheratio of tertiary industry output value for each alternative+
choiceThe
choice of individuals (1 for specific location, 0 for others)++
choiceAge of individuals+-Gender of individuals (1 for male 0 for female)+-Marrital
status of individuals (1 for married, 0 for others)+-

choiceage
malemarried

+
+
+
-+
+

Table 9. The results of Nested Logit Model for each province
Chongqing

Jiangsu

SichuanZhejiang

Anhui

Guangdong

Henan

Hubei

-3.070*-2.852***-2.252***-2.525***-0.408***-2.853**-0.000529***-0.00109***(1.587)(0.428)(0.514)(0.26)(0.0764)(1.311)(0.00013)(0.000174)

44.81

42.85***

41.13***

5.888

3.54

(30.28)(5.548)(10.96)(4.675)(13.61)

21.63**

17.44*

12.38*

31.93***

3.715***(9.513)(9.13)(6.908)(7.709)(0.786)-0.0317***-0.0791***-0.0434***-0.0413***-0.0480***-0.0738**-0.0470***-0.0466***(0.00865)(0.0105)(0.00477)(0.00968)(0.00409)(0.0359)(0.00536)(0.00642)-0.02710.344*

0.352***

0.425**-0.309***-0.0636

0.426***

0.340**(0.214)(0.197)(0.108)(0.215)(0.0831)(0.636)(0.11)(0.136)

0.696**

2.362***

1.109***

1.292***

1.036***

1.392

1.289***

0.779***(0.307)(0.329)(0.159)(0.348)(0.129)(1.035)(0.166)(0.197)

2.198

1.545***

1.689***

1.348***

0.192***

1.254-0.894***-0.765***41,54430,09635,29644,776

43,11238,784

Choice specificLogdistance

Tertiary

SecondMigrant
Age

Male

MarriedMiggrat_tau

Observations
19,00051,720Note: Figures in parentheses are t statistics.
*** Significant at the 1% level** Significant at the 5% level* Significant at the 10% level
Source: Author’s estimation.

Table 10. The signs of the coefficient estimates of the Nested Logit Model
Chongqing

Jiangsu

SichuanZhejiang

Anhui

Guangdong

Henan

Hubei

-

-

-

-

-

-

-

+

+

+

+

+

+

-

-

-

-

-

-

-

+

+

+

-

+

+

+

+

+

+

+

+

VARIABLES
Destination-specific variables
Log-distance-

Tertiary
Second+Migrant-specific variables
Age

-

Male

Married+Source: Survey Data and China Statistical Yearbook in 2008
From Tables 9 and 10, the general results are that the individual variables have
similar results with both prior studies and the prior results: young males tend to migrate.
The new findings from the destination choice model show that the choice specificvariables (moving distance, industry mechanism) have varied outcomes where distanceis a cost, and the second and tertiary industry ratios are attractiveness factors for
migrants.
In order to confirm the two stage structure of the nested logit model, the author
conducted a comparison test among the multinomial logit model, conditional logit
model, and nested logit model. Table 11 shows the results of the multinomial logit

model and nested logit model and presents the results of independence of irrelevantalternatives (IIA) test between the conditional logit model and nested logit model.
Although the Table 11 do not show supportive results for all of the provinces, especiallyfor the receiving provinces, the main sending provinces have the supportive results ofthis study.

Table 11. Test for specification of the choice specific variables and LR test for
Independence of Irrelevant Alternatives (IIA)
significance of migrants’ tau

Chongqing

Jiangsu

SichuanZhejiang

Anhui

0.768**

1.545*** 1.689*** 1.348*** 0.192***

Guangdong
1.254

Henan

Hubei

0.725*** -0.765***

Hypothesis :nested logit model better
yes

yes

yes

yes

yes

0.24

3.05*

4.87*

2.32

146.41***

yes

yes

than multi-nominal logit model(tau = 1), Prob > chi2

0.03

-718.89** 551.15****

Hypothesis :nested logit model better

yes

yes

yes

yes

yes

than conditional logit model

Note: Figures in parentheses are t statistics.
*** Significant at the 1% level** Significant at the 5% level* Significant at the 10% level

6 Conclusion and Discussion
In recent years, as a response to the migration and the urban development boom, theChinese government made a considerable effort towards labor policy making. With thegovernment’s continuous efforts, the situation of migrant workers has been improved,
though it still varies across provinces.
The first part of this study used a logit model to analyze the decision whether to stayin people’s hometown or migrate. The results indicate that the percentage for moving
decreases almost 25% when the consumption of the rural area increases 20%. Other
results show that people’s choice of migration is forced rather than a personal choice. Asignificant reason that is pushing people to move is an economic factor: the rural living
condition, which has long been neglected. This result suggests that the willingness tomove is low in relatively developed rural areas.
The second part of this study provides empirical evidence that the decision on
migration and destination is a joint decision making process. The effect pf
determination characteristics vary across provinces. On the one hand, the industry
mechanism (the ratios of secondary and tertiary industry to GDP) motivates migrants tomove from rural to urban areas. On the other hand, the geographic distance has anegative effect on the decision to move, i.e. the longer the distance is from thehometown to the destination, the lower the willingness to move will be.
While this paper suggests a new structure for study on China’s migration, there arestill opportunities for further studies. Since the nested logit model has been used foreach province separately, we suggest that future studies explore individual provinces toaddress their own special characteristics that influence migrants’ decision of destination
choice.

Reference
Brown, L.A., Moore, E.G.: The intra-urban migration process: a perspective. Geogr.
Ann. B 52, 1–13 (1970)
Connelly, R., & Maurer-Fazio, M. (2015). Left Behind, At Risk, and Vulnerable Elders
in Rural China: What the RUMIC Data Reveal about the Extent, Causes, andConsequences of Being Left Behind.
Dorigo, G., & Tobler, W. (1983). Push-Pull Migration Laws. Annals of the Associationof American Geographers,73(1), 1-17.
Fan, C. C. (2005). Modeling interprovincial migration in China, 1985-2000. Eurasian
Geography and Economics, 46(3), 165-184.
Fan, C. C. (2005). Interprovincial migration, population redistribution, and regional
development in China: 1990 and 2000 census comparisons. The Professional
Geographer, 57(2), 295-311.
Frank Qu, Z., & Zhao, Z. (2014). Evolution of the Chinese rural-urban migrant labormarket from 2002 to 2007. China Agricultural Economic Review, 6(2), 316-334.
Institute for the Study of Labor (IZA), Australian National University, the University of
Queensland and the Beijing Normal University (2014), Longitudinal Survey on
Rural Urban Migration in China (RUMiC) (2008 years of). IDSC of IZA.
http://dx.doi.org/ 10.15185/izadp.7680.1
Lee, B. H., & Waddell, P. (2010). Residential mobility and location choice: a nested
logit model with sampling of alternatives. Transportation, 37(4), 587-601.
Massey, D., Arango, J., Hugo, G., Kouaouci, A., Pellegrino, A., & Taylor, J. (1993).
Theories of International Migration: A Review and Appraisal. Population andDevelopment Review, 19(3), 431-466.

Rozelle, S., Taylor, J. E., & deBrauw, A.. (1999). Migration, Remittances, andAgricultural Productivity in China. The American Economic Review, 89(2),287–291.
Shuming Bao, Örn B. Bodvarsson, Jack W. Hou, Yaohui Zhao(2007). Interprovincial
Migration in China: The Effects of Investment and Migrant Networks. Chinese
Economy, 42 (4), 7-29.
Taylor, J. E., Rozelle, S., & De Brauw, A. (2003). Migration and Incomes in Source
Communities: A New Economics of Migration Perspective from China.
Economic Development & Cultural Change, 52(1), 75-101.
Vincenty, T. (1975) Direct and inverse solutions of geodesics on the ellipsoid withapplication of nested equations, Survey Review 22(176): 88-93.
Zhang, K. H., & Shunfeng, S. O. N. G. (2003). Rural–urban migration and urbanizationin China: Evidence from time-series and cross-section analyses. China
Economic Review, 14(4), 386-400.
Zhao, Y.. (1999). Leaving the Countryside: Rural-To-Urban Migration Decisions in
China. The American Economic Review, 89(2), 281–286.
Zhao, Y.. (1999). Labor Migration and Earnings Differences: The Case of Rural China.
Economic Development and Cultural Change, 47(4), 767–782.
Zhao, Yaohui (2003). The Role of Migrant Networks in Labor Migration: The Case of
China. Contemporary Economic Policy 21 (4): 500–511

Appendix

The education levels

The explanatory variables in the ‘migrate’ or ‘stay’ choice model numbers (from 1 to 8
stand for the levels of education).
Table A1: Levels of Education
Education

Freq.

Percent

Cum.

2,143

6.72

6.72

elementary school

7,719

24.2

30.92

Junior middle school

15,826

49.63

80.55

Senior middle school

3,877

12.16

92.71

Vocational school

1,483

4.65

97.36

Polytechnic college

672

2.11

99.46

Undergraduate

161

0.5

99.97

Postgraduate

10

0.03

100

31,891

100

Never attended

schoolTotal

Source: RUMiC Survey Data in 2008

The macroeconomic data of provinces

Table A2: The macroeconomic data for the destination choice model
Province

Secondary industry

Tertiary Industry

GDP

Jiangsu

23079.18

19760.76

45051.84

0.512281

0.438623

Zhejiang

11567.42

8799.3121462.69

0.538955

0.409982

Anhui

4198.933234.64

8851.66

0.474366

0.365428

Henan10259.99

5099.76

18018.53

0.569413

0.283029

Hubei

5082.074466.8511328.92

0.448593

0.394287

Guangdong

18502.2

16321.46

36796.71

0.502822

0.443558

Chongqing3057.82160.485793.66

0.527784

0.372904

Sichuan5823.394561.6912601.23

0.462129

0.362004

Source: China Statistical Yearbook in 2008

Second ratios Tertiary ratios

The calculation of the distanceThe distance from the home to destination provinces of migrants between “Hukou”(their hometown), and destination provinces are calculated using the gaining thelongitude and latitude information from Google Maps. In this study, the geographic
distances is caulculated as the length of the shortest curve between two points along thesurface of a mathematical model of the Earth. By default, this paper used the input
coordinates that are assumed to be based on the WGS 1984 datum (the same used by
Google Earth/Maps and GPS devices), and calculated the ellipsoidal distances using
Vincenty's (1975) equations (Given the coordinates of the two points (Φ1, L1) and (Φ2,
L2), to calculate the ellipsoidal distance s. s=bA(σ-∆σ) ). This assumes a sphere with aradius of 6,371km to approximate the shape of the earth and can computes great-circle
distances. The distance is shown in Table 1.

Table A3: Provinces of geographic information
Province

Google Maps GPS Coordinates
N

E

Henan

34.7655-113.754

Jiangsu

32.0617-118.763

Sichuan

30.6512-104.076

Hubei

30.5466-114.342

Anhui

31.8611-117.285

Shanghai31.2304-121.474

Zhejiang

30.2674-120.153

Guangdong

23.1322-113.267

Chongqing

29.563-106.552

Source: Google MapsIn this research, the size of each province is shown as follows. For those who chose tostay, the moving distance has been calculated as the radius of their hukou province.

Table A4: Size of each provinceSize(km²) of provinces
Province

From National Bureau of Statistics of the People’s Republic of China

Henan

167,000

Jiangsu

102,600

Sichuan

485,000

Hubei

185,900

Anhui

139600Shanghai

7,037

Zhejiang

101,800

Guangdong

177,900

Chongqing

82,300Source: Google Maps and National Bureau of Statistics of the People’s Republic of China

Table A5: The calculated moving distance for those who chose to stay
Province

Radius(km)Henan

230.5596

Jiangsu

180.7169

Sichuan

392.9126

Hubei

243.2567

Anhui

210.7986

Zhejiang

180.011

Guangdong

237.965

Chongqing

161.8546

Source: Author’s calculation using the coordinates information in Google Maps

Statistics for Nested-logit model
Table A6: The explanatory variable for nested logit model for each province
SichuanZhejiang

Variable

Obs

Mean Std. Dev.

Min

Max

Obs

Mean Std. Dev.

Min

Max

Age

41544 37.47006 18.68832

1

9730096 39.64115 18.53832

1

96

Male

41560 0.524735 0.499394

0

130096 0.523392 0.499461

01

Married41560 0.620019 0.485388

0

130096 0.653642 0.475817

01

Choice41560 0.125 0.330723

0130096 0.125 0.330724

01

Second41560 0.504543 0.038464 0.448593 0.569413 30096 0.504543 0.038464 0.448593 0.569413

Tertiary

41560 0.383727 0.048035 0.283029 0.443558 30096 0.383727 0.048035 0.283029 0.443558

Log_distance 41560 6.462717 1.248251 3.457217 7.339688 30096 6.245932 0.971373 4.24604 7.339688

Chongqing

Jiangsu

Variable

Obs

Mean Std. Dev.

Min

Max

Obs

Mean Std. Dev.

Min

Max

Age

19000 38.73642 18.79175

1

9251720 37.38237 18.2958

1

97

Male

19000 0.522105 0.499524

0151720 0.516628 0.499728

01

Married19000 0.660211 0.47365

0151720 0.636814 0.480923

01

Choice

19000

0

1

51720

0

1

Second19000 0.504543 0.038464 0.448593 0.569413 51720 0.504543 0.038464 0.448593 0.569413

Tertiary

19000 0.383727 0.048035 0.283029 0.443558 51720 0.383727 0.048035 0.283029 0.443558

0.125 0.330728

0.125 0.330722

Log_distance 19000 6.428889 0.928027 4.303762 7.17964 51720 6.074296 0.993703 4.369928 7.245877

anhui
Variable

Obs

Age

Mean

Std. Dev. Min

guangdongMax

Obs35296 34.77403 18.15296 1

9544776 33.53904 18.78814 1

102

Male

35304 0.511897 0.499866 0144880 0.520856 0.49957 0

1

Married35304 0.586902 0.492397 0

144880 0.495722 0.499987 01

Choice35304 0.125

144880 0.125

1Second35304 0.504543 0.038464 0.448593 0.569413 44880 0.504543 0.038464 0.448593 0.569413

Tertiary

35304 0.383727 0.048035 0.283029 0.443558 44880 0.383727 0.048035 0.283029 0.443558

0.330724 0

Mean

Std. Dev. Min

0.330723 0

Max

Log_distance 35304 5.843096 1.264499 3.063435 7.140438 44880 6.661319 0.843075 4.456937 7.165823

Source: 2008 Survey Data and China Statistical Yearbook