Market Demand of Migration and Floating Migration: Using Micro Data of
the Chinese Household Income Project※

Yang Liang and Suminori Tokunaga
Graduate School of Life and Environmental Sciences, University of Tsukuba
1. Introduction
One of the most prominent characteristics of the Chinese economic development during the last 30
years has been the farmers’ mobility from rural to urban areas. Farmers’ mobility includes two categories(Liang and Tokunaga [8]): 1) Migration (a permanent mobility, which is used for a farmer who moves from
hometown to other areas and changes the hukou1)); 2) Floating migration (a temporary mobility and the
hukou remains unchanged). The National Central Economic Conference in 2009 issued that easing of
hukou restrictions in medium and small cities and towns was one of the government’s intentions in 2010.
Previous studies have estimated the determinants of mobility in terms of the supply of laborer, such as
the individual, household and home region characteristics and so on (Borjas [1], Chiquiar and Hanson [2],Laszlo and Santor [7], Munshi [9] and Wu [13]). However, it is greatly necessary to consider the demand oflaborer based on the factors of market economy. Initiated by Fujita et al. [4] and Krugman [6], New
Economic Geography theory analyzes the spatial location of economic activity. Although a great deal of
empirical literatures has followed this theory, the new economic geography has not yet generated anempirical analysis on Chinese farmers’ mobility for finding a job in the city. The Population Census
database used in previous studies refers to individuals’ movement not only for employment in the city, butalso for marriage, education and visiting the relatives. This paper aims to study the determinants of
migration and floating migration form rural to urban areas based on the factors of market demand oflaborers and to focus on the access to market and distance between the origin to the destination.
2. Migration and floating migration based on the micro data
Table 1. Characteristics of migration and floating migration
Migration
Observations
Destinations

Code in CHIP
11
14
21
32
34
41
42
44
50
51
53
62

Persons
101
Province
Beijing
Shanxi
Liaoning
Jiangsu
Anhui
Henan
Hubei
Guangdong
Chongqing
Sichuan
YunnanGansu

Education
University and above
Junior college
Vocational school
Senior high school
Junior high school
Elementary school
Less than elementary school

Floating migration
%
3.40
%
13.9
7.9
14.9
14.9
6.9
7.9
2.0
16.8
3.0
3.0
4.0
5.0
%
6.94
10.89
10.89
25.74
33.66
7.92
3.96
%

Code in CHIP
11
14
21
32
34
41
42
44
50
51
53
62

Persons
2889
Province
Beijing
Shanxi
Liaoning
Jiangsu
Anhui
Henan
Hubei
Guangdong
Chongqing
Sichuan
YunnanGansu

University and above
Junior college
Vocational school
Senior high school
Junior high school
Elementary school
Less than elementary school

Main sectors
Source:the authors’ calculation based on the CHIP database [5].

%
96.60
%
4.7
6.0
11.2
9.5
10.3
9.3
10.0
10.4
4.8
7.6
8.4
7.7
%
0.31
1.32
3.29
11.6
49.01
23.64
10.83
%

The Chinese Household Income Project [5] summarized the survey in urban areas and focused on
farmers who have moved from rural to urban areas with standard demographic variables (including the
hukou categories) as well as economic variables such as medical insurance, expenditures and employment
information. Due to incomplete information and false results, 2990 valid individual questionnaires werechosen from the CHIP database. They come from rural areas in most of provinces of China (except Hainan,
Tibet and Xinjiang Provinces) and move to the city of 12 provinces, shown in Table 1.
The hypotheses are proposed: (1) The costs of mobility have an important effect on the decision ofmobility and could be proxied by the distance between the home region and the host region. The costs ofmobility will increase as the far distance. Moreover, intra-province is supposed as the dummy variable witha positive effect on mobility. (2) As proved in Crozet [3], workers are more likely to move to the regionswith a large market potential due to the low living cost resulted from the price index effect.
3. Theoretical framework
We follow the model of the determinants of mobility in Crozet [3], Tabuchi and Thisse [11]. A mobile
worker k from region j and his location choice among R regions ( j ∈ R ) are considered. We
suppose that the mobility decision is decided to maximize the following objective function:−λπ kji ,t = V jik,t + ε ik = ln ωi ,t −1 ρi ,t −1  dij (1 + bFij )   + ∈ik



(1)



where V ji ,t denotes the perceived quality of life in region i at date t for a woker k and ∈i is akkstochastic k ’s personal characteristics in region i . We set ωi ,t −1 as the real wage in region i at datet − 1 ; ρi ,t −1 as the employment probability for a mobile individual region i at date t − 1 ; dij (1 + bFij )  as a mobility cost which increases with the distance between home j and host region
i . λ is the distance elasticity of mobility cost and b is the strictly positive coefficient.
−λWe assume that a mobile individual k make the decision depending on the comparison of π ji ,tkamong regions at date t − 1 . Therefore, if V jik,t −1 > V jrk ,t −1 , ∀i ≠ r , k will choose to move to region i . The
probability of choosing region i is obtained as following:P ( M ji ,t ) = e ji ,t−1 ∑ r =1 e jr ,t−1
Vk

R

Vk

(2)

The share of individuals with mobility from region j to region i is:∑

Vkmigrji ,ti '≠ jmigrji ',t=e ji ,t −1∑ eR

V jrk ,t −1

r =1

Vk

− e jj ,t −1(3)

We assume that each region in the market produces three categories of goods: manufactured goods x ,non-traded services y and a homogeneous traditional good z . The price of z is numeraire, so pz = 1
in all regions.
Therefore, the real wage of mobile individuals in region i is:ωi ,t =wi ,tφ

Pyi ,t Pxiµ,t(4)where wi ,t is the mobile wokers’ wage in region i at date t ; φ and µ are expenditure share ofmanufactured goods x and non-traded services y respectively. Pxi ,t and Pyi ,t are the CES priceindex of the aggregate of manufactured goods x and non-traded services y respectively in region i at
date t , which are shown as following functions:
1−σ x R
Pxi ,t =  ∑ nxr ,t ( Bdirδ pxr ,t )  r =1
1/ (1−σ )Pyi ,t = n yi ,t y p yi ,t1/ (1−σ x )

(5)
(6)

τ ij = Bdijδ , ∀i, j ∈ [1, R] , δ > 0(7)where we follow the iceberg transport costs in transporting goods between regions and propose(τ − 1) τij

ij

of the goods melts away and so τ ij (shown in the equation (7) ) units of goods should be

shipped from region i to transport 1 unit to region j . δ is the elasticity of trade costs to distance.

pxi ,t and p yi ,t are the FOB price of goods in region i at date t :pxi ,t =σxβw
σ x − 1 x i ,t(8)

p yi ,t =σyβ w
σ y − 1 y i ,t(9)where σ denotes the elasticity of substitution between varieties; β is the marginal input requirementsfor production.

nxi ,t and n yi ,t are the number of firms of goods x and y respectively in region i :nxi ,t =nyi ,t =

Lix,t

(10)

ε xσ xLiy,t(11)

ε yσ ywhere L is the total employment in each region; ε is the fixed input requirements for production.
Using equations (4), (5), (6), (8), (9), (10), (11), the share in equation (3) could be written as:µ (σ x −1)
 migrji ,t
1−σ x φ (σ y −1)
R x
y
δ


ln
= ln ( Li ,t −1 )+ + ln ∑ ( Lr ,t −1 )( dij wr ,t −1 )  ∑ migrji ',t  r =1 i '≠ j+ ln  wi1,−t −φ1 ρi ,t −1  + ln  dij (1 + bFij ) −λ~+ a j ,t −1

(12)

~

where a j ,t −1 is the characteristics of the home region j :4. Model specification and data
We estimate the following specification of equation:

 migrji ,t 
 = β1 log ( Li ,t −1 ) + β 2 log ( probwi ,t −1 ) + β3 log ( dij )log  ∑ migrji ',t  i '≠ j+ β 4 Fij + β 5 log ( Si ) + β 6 Munic + β 7 Intra+ β 8 Humani ,t −1 + β 9 Human j ,t −1 + u ji ,t(13)

Table 2 defines the independent variables. Distance between the home region and the host region is
proxied by the nearest railway distances between their respective capital cities. The distance of
intr-provincial mobility is proxied by dii = ( 2 3) Si π , where Si is the surface of region i .
Table 2. Independent variables description
Independent
variables

Descriptions

Sign

Li ,t −1

Ln of ( the total employment in region i at date t-1)-/+

probwi ,t −1

Ln of ( nominal wage×employment rate in region i at date t-1)

+

d ij

Ln of ( distance between region i and j)-

Fij

Dummy variable of no-border, which is one if region i and j do not share acomman border and zero for otherwise-Si

Ln of ( surface of region i)

+

Munic

Dummy variable, which is one if region i is the municipal province andzero for otherwise
Dummy variable, which is one if region i and j are in the same provice andzero for otherwise

Intra

+
+

Humani ,t −1

Average years of education in region i at date t-1-

Human j ,t −1

Average years of education in region j at date t-1-

5. Results
The effect of costs of mobility on the mobility is estimated through 3 independent variables: Firstly,
distance plays the expected statistically negative role in mobility. The high value of distance indicates how
reluctant farmers are to move to a distant province for work. It looks like that the costs of mobility may
increase as the distance between two regions. This result is consistent with the finding in Crozet [3].
Secondly, no-border is the statistically positive factors affecting the mobility. It implies that farmers are
more likely to move by crossing more than 1 regional border. Thirdly, intra-province is estimated in column(6). This coefficient is statistically positive affecting migration with a large marginal effect (44%).
The number of total employment in the host region has been always statistically important andnegative factor. This variable indicates the market size in the host region and suggests that farmers do
prefer small economic regions. Although the policy makers have loosened the hukou system, mobility to
the large central business district is still limited in terms of the current policy. Therefore, we can conclude
that as the proxy of market potential, total employment is statistically significant and negative; farmers in
China prefer not far away provinces with small regional markets. We can not obtain the significant effect of
expected wage in the host region in all cases. Although there are prominent rural-to-urban income gap in
China, farmers are more concerned with the costs of mobility and market size. As the increasing
rural-to-urban mobility due to the loosened hukou system, there is serious competition for farmers to find ajob in a city, they pay more attentions to finding a job in a near region. Human capital in the host region
plays a statistically significant and negative effect on the mobility, which implies that farmers are more
likely to move to the region with a low educational level due to less education of their own.
Above all, as expected, the costs of mobility reduce the mobility flow by the proxy of distance
between the home region and the host region and intra-province mobility. Moreover, we conclude that
farmers have not yet followed with large market potential.

Table 3. Result of the determinants of migration and floating migration
 migrji,t
Dependent variable: log 

migr
∑
ji
',
t
 i '≠ j

Total employment

log ( Li ,t −1 )(1)Coefficient Marginal(SE)
Effect-0.119 *** -0.119(0.027)(2)
Coefficient Marginal(SE)
Effect-0.124 ***Primary industry employment

log ( L1i ,t −1 )(3)
Coefficient Marginal(SE)
Effect-0.054 **-0.054-0.091 ***-0.001-0.001-0.231 ***No-border

0.076 ***

Fij

(0.029)Surfacelog ( Si )(0.018)0.112 ***

-0.019-0.019(0.031)-0.231(0.015)-0.239 ***0.069-0.2390.178-0.005-0.091-0.226-0.226***(8)
Coefficient Marginal(SE)
Effect-0.118 *** -0.118(0.027)-0.226

**

0.069

0.081

*** -0.226

***

0.178

0.068

0.024

0.024

0.037

***

0.081

-0.227 ***0.080-0.227

***

0.068

0.078

0.006

-0.232

***

0.080

0.078

*** -0.232

***

0.078

0.087

***

0.078

0.037

Municipal province-0.107(0.086)-0.069-0.109

***

0.087

0.059

0.015

0.015

(0.053)**-0.069-0.244**-0.1090.069*** -0.244

***

0.059

0.207

-0.233Intra-province

0.440(0.086)

**

0.069

***

0.075

-0.056(0.031)

***

0.207

0.112

*

-0.020(0.021)

No. observations–

1.454 ***
(0.520)–1.190 **
(0.499)–1.897 ***
(0.598)–

1.195 **
(0.494)–0.403(0.611)–0.112

1.362 ***
(0.523)

514

514

514

514

514

514

514

514

R-sq

0.357

0.372

0.338

0.342

0.355

0.386

0.383

0.355

Root MSE
F-stat

0.304
55.58
0.000

0.300
60.17
0.000

0.208
51.77
0.000

0.307
52.85
0.000

0.304
46.62
0.000

0.378
53.03
0.000

0.298
39.23
0.000

0.304
46.44
0.000

Notes: ***, **, *, Statistical significant at 1%, 5%, 10% level.

***-0.056

Human capital (home)
*

0.075

(0.019)Human j ,t −1

0.935(0.488)

***

0.440

Human capital (host)–-0.233(0.029)(0.032)

Humani ,t −1

1.496 ***
(0.503)***-0.107

Intra

Constant

0.018(0.015)(0.029)(0.021)0.018(0.038)(0.015)(0.046)(0.027)

Munic-0.012(0.035)(0.029)(0.016)-0.012(0.031)(0.015)(0.029)(0.015)0.006(0.032)(0.015)(0.029)(0.024)0.094(0.108)(0.033)(0.015)(0.029)0.112-0.005(0.032)(0.015)0.076

Marginal
Effect(0.073)(0.028)(0.032)(7)
Coefficient
(SE)

0.094

(0.021)

Tertiary industry employment

Distcane(6)
Coefficient Marginal(SE)
Effect-0.114 *** -0.114(0.027)(0.043)log ( L3i ,t −1 )log ( dij )(5)
Coefficient Marginal(SE)
Effect-0.142 *** -0.142(0.033)-0.124

Secondary industry employment

Expected nominal wage

Marginal
Effect(0.021)log ( L2i ,t −1 )log ( probwi ,t −1 )(4)
Coefficient(SE)-0.020–6. Conclusions
Based on the access to market initiated by the new economic geography theory, we estimated thedeterminants of migration and floating migration in China by running a new economic geography model
based on the latest CHIP database. 2990 farmers have moved from rural to urban areas from 1993 to 2002.
They comes from rural areas in most of provinces in China and moves to the city in 12 provinces, including
Beijing and Chongqing as the province-level municipalities, Shanxi, Liaoning in the north, Jiangsu andGuangdong in the eastern coastal areas, Henan, Hubei and Anhui in the interior, Sichuan, Yunnan andGansu in the west. 96.6% of farmers with mobility has participated in floating migration, and merely 3.4%
of farmers with mobility has taken part in migration.
This paper provided an empirical study of the determinants of migration and floating migration
particularly concerned with the costs of mobility and the market potential. We found that total employment,
employments in the primary industry, secondary industry and tertiary industry, distance and human capitalin the host region are statistically significant and negative factors affecting the mobility. No-border, surface
and intra-province play statistically significant and positive role in the decision of mobility. Moreover, themost important finding in this paper was that, the costs of mobility reduce the mobility flow by the proxy of
distance between the home region and the host region and intra-province mobility. Especially, according to
the statistically significant and negative effect of total employment in the host region on the mobility, we
concluded that farmers have not yet followed with large market potential.
Note:1) Hukou: It refers to the household registration system in China. This system classifies citizens into two categories: urban(nonagricultural) and rural (agricultural) residents within a municipality.

Acknowledgments
The second author has been supported by grants from the Japan Society for the Promotion of Science (JSPS)(No.20530190).
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