Chan Migration China MD

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Migration and development in Chinatrends geography and current issuesKam Wing Chan aa Department of Geography University of Washington SeattleWA USA

To cite this article Kam Wing Chan (2012) Migration and development in China trends geographyand current issues Migration and Development 12 187-205

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Migration and development in China trends geography and currentissues

Kam Wing Chan

Department of Geography University of Washington Seattle WA USA

(Received 13 August 2012 final version received 14 September 2012)

Chinarsquos recent meteoric rise in the global economy is closely related to the strength of itsmanufacturing sector which is heavily dependent on cheap migrant labor This paper ana-lyzes Chinarsquos recent migration trends spatial pattern and their relationship with Chinarsquoseconomic strategy Internal migration in China is special in that it is heavily controlled andregulated by the hukou (household registration) system The system enables the country tocreate a massive exploitable migrant labor force that makes Chinarsquos industry highly com-petitive in the global economy The paper explains how the system works and distils thecomplex population and migration statistics to present a relatively complete picture ofmigration over time and space including the latest changes Special focus is on analyzinglsquorural migrant laborrsquo which has constituted the most important human cog powering theChina economic engine Long-distance interprovincial migration is also studied in the con-text of the changes in the regional economy in supporting Chinarsquos ascendancy to being thelsquoworldrsquos factoryrsquo Three major issues pertaining to this migrant labor system and recentdevelopments in China and the global economy are examined They presage importantchanges to come which are likely to impact both China and the rest of the world

Keywords China migration hukou system interprovincial migration worldrsquos factory

1 Introduction

Chinarsquos rise as the lsquoworldrsquos factoryrsquo is definitely one of the biggest stories of the 21st CChinarsquos dominance in manufacturing has made it a major player in the global economy ThisChina success story is closely intertwined with the migration story without the epic-scalemigration of peasants ndash which supplies almost infinite low-cost human labor to power theChina economic engine ndash the ascent of China would be totally unthinkable Cheap migrantlabor is what makes lsquoChina pricersquo so unrelenting (Harney 2008) The last three decades havewitnessed the worldrsquos lsquoGreat Migrationrsquo ndash an estimated 200ndash250 million rural residents havemoved to cities and towns within China (Chan 2012a) To put this in perspective the volumeof the Great Migration of Europeans to North America from 1800 to the World War I wasonly a fraction of Chinarsquos lsquoon the order of fifty million personsrsquo (Tilly 1976 p 58)

Building on the previous work (Chan 2001b 2012b Liang 2007) and using the latestCensus data available this paper analyzes Chinarsquos recent migration trends spatial pattern andtheir relationship with economic development Chinarsquos internal migration is special in that itis heavily controlled and regulated by the hukou (household registration) system (Chan 1994Fan 2008) As will be explained below through the special institutional design of the hukou

Email kwchanuwedu

Migration and DevelopmentVol 1 No 2 December 2012 187ndash205

ISSN 2163-2324 printISSN 2163-2332 online 2012 Taylor amp Francishttpdxdoiorg101080216323242012739316httpwwwtandfonlinecom

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system China has also managed to turn this vast number of rural-urban migrants into thelargest army of cheap industrial labor the world has ever seen Moreover Chinarsquos simulta-neous use of a de jure hukou-based population registration system and de facto populationstatistical counts have greatly complicated the task of counting especially migrants and mea-suring migration There are all kinds of conceptual and technical complexity plus a plethoraof lexicon both of which serve mostly to mislead and confuses some researchers (see Chan2007 Liang 2007 Liu amp Chan 2001) This requires some explanation before one cananalyze the trends and patterns The concluding section of this paper highlights the latestimportant migration issues affecting Chinarsquos development and the world

2 The hukou system1 and migration

After the Communist Revolution in 1949 China opted for the traditional Stalinist growthstrategy of rapid industrialization centered on heavy industry in cities and extraction of agri-cultural surplus from the peasantry (Chan 1994) This strategy was predicated upon exploit-ing the rural labor by denying it geographic mobility and access to state welfare From themid-1950s the government repeatedly introduced measures to stem rural outflows culminat-ing in 1958 in the formal codification of a comprehensive registration system to control pop-ulation mobility (Chan 2009a Wang 2005) The regulation decreed that all internalmigration be subject to approval by the relevant local government From that point Chinesecitizens lost the freedom of residence and migration within their own country Each person isassigned a hukou (registration status) classified as lsquoruralrsquo or lsquourbanrsquo in a specific administra-tive unit The hukou mechanism served a central instrument of the command system designedfor the big-push industrialization to prevent peasants from exiting from the countryside Thisindustrialization strategy led China to create in effect two very different societies on the onehand the urban class whose members worked in the priority and protected industrial sectorand who had access to (at least basic) social welfare and full citizenship and on the otherhand the peasants who were tied to the land to produce an agricultural surplus for industriali-zation and who had to fend for themselves Hukou conversion referring to change from therural to the urban category was tightly controlled and permitted only under very limitedconditions usually when it was in accordance with state industrialization plans The hukousystem was not merely a means of limiting rural-urban population and labor mobility as ithas been commonly depicted but also a system of social control aimed at excluding the ruralpopulation from access to state-provided goods welfare and entitlements so that the ruralpopulation segment remains cheap and easily exploited

It was under this socioeconomic context that China in the late 1970s launched its eco-nomic reform After some experimentation as China latched onto a labor intensive export-oriented growth strategy in the mid-1980s rural labor was allowed en masse to the cities tofill industryrsquos labor demand which later became a major state industrialization strategy Bythe mid-1990s rural-hukou labor had become the backbone labor force of the export industrybased on manufacturing Today rural hukou labor also staffs almost all of the low-end ser-vices in urban areas In coastal export centers such as Shenzhen and Dongguan migrant labornow accounts for by far the greater part (70ndash80) of the labor force (Chan 2009b Liang1999)

lsquoRural migrant laborrsquo (nongmingong) as the term comes to be known in China has a spe-cific meaning in the country it refers to industrial and service workers with rural hukouThese village-origin laborers though working on urban jobs and residing for the most part intowns and cities are not considered legally to be urban workers Neither are they (nor underthe current system will they one day be) treated as lsquolocalsrsquo rural migrant is not a probational

188 KW Chan

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12

status but permanent (Wu 2011) They are not eligible for regular urban welfare benefits andsocial services (access to local schools urban pension plans public housing etc) and otherrights that are available to people with urban hukou Instead rural migrant workers are treatedofficially as part of the rural hukou population even though they may have worked and livedin an urban area for many years In short they are trapped through institutional mechanismin a permanent social lsquohalf-arrivalrsquo situation belong to the netherworld of rural or urban andwith little hope of acculturating into the urban permanent population (Saunders 2012 Zhang2012) This also applies to their children even some of them are born in the city The legallylsquotemporaryrsquo status of this grouprsquos members and their permanent ineligibility for locallsquocitizenshiprsquo in the form of urban hukou make them forever vulnerable and easily expendable(Solinger 1999 Wu 2011)

Onersquos hukou classification remains unchanged no matter where heshe moves unless heshe effects a formal hukou conversion which is almost impossible to get for an ordinary ruralmigrant As a result of this institutional design they are consigned to low-end factory andservice jobs In many cities and export zones local decrees have also forbidden migrantsfrom taking up jobs other than those in the low-skilled 3-D (dangerous dirty and demeaning)category The denial of local urban hukou to migrant workers combined with their plentifulsupply and lack of access to legal support has created a large easily exploitable yet highlymobile and flexible industrial workforce for Chinarsquos export economy The internal migrantlabor force is equivalent to the cheap migrant labor in the classical Lewis model of theunlimited supply of labor It has greatly contributed to Chinarsquos emergence as the worldrsquoslsquomost efficientrsquo ndash ie the least-cost ndash producer Figure 1 outlines the main components of Chi-narsquos dual society with particular reference to position in the social (and economic) hierarchy(pyramid) type of hukou (urban or rural) and ruralurban location in two different historicalperiods ndash Maorsquos (pre-1979) era and the present

Alarmingly their numbers have been swelling rapidly even excluding those employed intownship or village enterprises close to their home villages the size of the rural migrant laborforce has expanded from about 20ndash30million in the 1980s to close to 160 million in 2011(see Table 1 later) The total number of (urban) population without local hukou was even

Figure 1 Main components of Chinese society with reference to the social hierarchy hukou type andlocation in ruralurban area

Migration and Development 189

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Table

1Major

aggregatemigratio

nandurbanpo

pulatio

nstatistics19

82ndash2011(inmillions)

HukouMigrants

(Yearlyflow

figures)

Non

-HukouPop

ulation

(Stock

figures)

Defactopopulatio

nin

urbanareas

lsquoTem

porary

Populationrsquo

lsquoRural

Migrant

Laborrsquo

Rural-hukou

populatio

nin

urbanareas

Policestatistics

Registeredwith

MPS(m

id-year)

NationalCensusesMini-Censuses

NBSestim

ates

basedon

samplesurveys

Authorrsquosestim

ates

NBSstatistics

Geographicboundary

(tocross)

Citytownor

township

Township-

levelunit

Countyor

city

Tow

nship

Not

applicable

Not

applicable

Minim

umlength

ofstay

(for

those

with

outlocalhukou)

Nominim

um3days

6months

6months(unless

otherw

isespecified)

Away

foroutsidework

(for

6monthsor

more)

6months

6months

Series

AB

CD

EF

G(

)H

1982

1730

66(1year)

465

217

2145

1987

1973

152

a260

c640

231

2767

1990

1924

216

(1year)

663

220

3020

1995

1846

497

291

b750

694

197

3517

2000

1908

448

1444

1210

1366

298

4591

2001

1701

551

1486

309

4806

2002

1722

598

1047

1528

304

5021

2003

1726

699

1139

1495

285

5238

2004

1949

780

1182

1514

279

5428

2005

1933

867

1531

1258

1531

272

5621

2006

2060

953

1321

1564

271

5771

2007

2084

1044

1370

1630

275

5938

2008

1892

1166

1404

1670

275

6067

2009

1677

1222

1453

1716

276

6219

2010

1701

1314

2614

2214

1534

2056

309

6656

2011

1554

1586

6908

Notes

andsources

MPS=Ministryof

Public

Security

NBS=NationalBureauof

Statistics

a the

geographic

boundary

isbasedon

citycounty

ortown

bthegeographic

boundary

isbasedon

county-level

units

c refersto

1988

AMPS(1988ndash

2010)NBSamp

MPS(1988)BMPS(1997ndash

2011)C

andDNBS(1988)SC

ampNBS(1985

1993200220072012)andNPSSO

(1997)E2002

ndash2008figuresarefrom

NBS

compliedby

Cai

andChan(2009

Table1)2009


NBS(2010

2011

and2012a)Earlierfiguresareestim

ates

inLuet

al(2002)

andthey

may

notbe

fully

comparableto

recent

NBSfiguresFEstim

ates

derivedfrom

Chan(2012aTable1)GFexpressedas

apercentage

ofHHNBSfigurescompiledby

Chan(2012a)2011

figure

isfrom

Ma(2012)

190 KW Chan

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higher reaching 230 million in 2011 (NPFPC 2012) Again to put the number in perspec-tive the total number of international migrants is estimated by the United Nations (2012Table 1) to be 214 million in 2010 which is smaller than Chinarsquos non-hukou population inurban areas The rapid expansion of Chinarsquos essentially disenfranchised population primarilyin urban areas has become its hallmark in the last quarter century

Based on the above two broad categories of migrants can thus be identified in China(Chan Liu amp Yang 1999)

(a) Migration with lsquolocalrsquo residency rights (hereafter hukou migration) This is usuallyopen only to a select group (currently the rich or the highly educated) and immediatefamily members of residents with local hukou (Chan amp Buckingham 2008)

(b) Migration without hukou residency rights (non-hukou migration)

Officially only hukou migration is considered qianyi (migration) Migrants in that categoryare eligible for the same array of social benefits and rights other local residents have Othertypes of moves are considered renkou liudong (population movements or lsquofloatingrsquo popula-tion) implying a lsquotemporaryrsquo move to a destination where the person is not supposed to andis legally not entitled to stay permanently Chinarsquos exceptionally large numbers of peoplemoving internally as well as the circulatory and temporary nature of some of them havehugely complicated the efforts to measure the movement consistently and to address accu-rately its many implications (Chan 2007 2012b)

Based on the above understanding and a careful differentiation of migration urbanizationand hukou statistics gleaned from a variety of sources and surveys2 in accordance with theirnature (flow or stock) and temporal and geographic coverage a matrix set up in Table 1allows us to make good sense of those numbers and gain an understanding the overall vol-ume of migration its variety and trends in the last 30 years Despite the diverse sources andthe varying quality of these data sets when analyzed side by side they not only becomemeaningful and useful information but also display some notable broad consistencies asexplained next

Hukou Migrants Series (A) This series refers to hukou migrants and is the only lsquoflowrsquodata series presented in Table 1 The figures refer to the number of in-migrants who are for-mally granted hukou status in the destination (city town and township) each year The dataare from Ministry of Public Security They represent the total number of all types officiallyapproved changes in hukou (residence) within a particular year from townships to cities fromcities to cities from townships to townships etc most probably excluding moves within cit-ies towns and within townships A portion of hukou migration is rural-to-rural migrationmainly involving marriage

Non-Hukou Population Series (Series BndashG) This refers to the people staying in anadministrative unit (usually city town street or township) other than their place of hukouregistration This group is also commonly known as the liudong renkou (floating population)The lsquofloating populationrsquo is not the de jure population In some cases (such as Shenzhen) thesize of the floating population is far larger than that of the de jure population (Chan 2009b)The non-hukou population series are migrant lsquostockrsquo figures ie the number of non-hukoumigrants who reside in a certain locale at a given point in time

Owing to the different purposes coverage and criteria used in defining the geographicboundary and the minimum duration of stay the statistics for the non-hukou population ineach series may be expectedly quite different even for the same year

Series B This is a systematic series of non-hukou population based on actual registrationstatistics kept by the Ministry of Public Security By law anyone staying in places other than


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hisher place of household registration for three days or more is required to register with thepolice and apply for a zanzhu zheng (temporary resident permit) Consequently this group isalso categorized as lsquotemporary populationrsquo (zanzhu renkou) by the police authorities In real-ity a large number of lsquofloatersrsquo fail to comply with this requirement that helps to explain partof the large discrepancies between Series B and C

Series C and D These are statistics collected by the National Bureau of Statistics (NBS)through censuses and mini-censuses (1 national surveys) The population is definedbasically on a de facto basis The de facto definition stipulates a minimum residence require-ment (6months or one year) to define a changzhu population (de facto resident population)the residence period is far longer than the one used in Series B The series are also commonlyreferred to as lsquotemporary populationrsquo (Yang 1996) although many in this group stay farlonger that what would commonly be considered as lsquotemporaryrsquo These two series howeverexclude the truly temporary such as tourists and shorter-term migrant workers Because Calso includes people moving within the same cities who are not truly lsquonon-hukou populationrsquoas we generally understand it D is the better statistic for representing the non-hukoupopulation

Series E This series refers to what is known as lsquorural migrant laborrsquo (nongmingong)which is the largest constituent group of the non-hukou population This group includes onlythe working population with rural-hukou (and without local hukou in the destination)3 almostall of them work in cities and towns and are away from home villages for usually six monthsor more The figures are estimates based on large-scale sample surveys conducted in the ruralareas Despite this grouprsquos importance systematic and consistent national surveys of thisgroup by the NBS did not take place until the early years of the 21st C Estimates for thepre-2000 period are less systematic and based on smaller samples and somewhat differentdefinitions The majority of rural migrant laborers are unskilled or low-skilled workers Someof these rural migrants are seasonal and are therefore prone to move between the city and thecountryside 2ndash3 times in a year They are also harder to be enumerated using standard popu-lation counting procedures4

Series F and G F is an estimate of those carrying rural-hukou but staying in the urbanarea The number is computed as the difference between the de facto urban population (SeriesH) and the lsquononagricultural-hukou populationrsquo published by the Ministry of Public SecurityThe latter is commonly considered as the urban-hukou population (see Table 1 in Chan2012a) Series F is closely related to Series E broadly the former is the sum of E (ruralmigrant laborers) and their dependents (non-working population) Series F also overlaps withthe urban population without local hukou to a large extent The latter figure in 2010 asreported by NPFPC (2011) for instance is 221million which is very close to the figure in Ffor the same year (206million) G is F expressed as a percentage of H

Urban Population Series (H) This is added here to show the growth of the urban popula-tion an important metric of Chinarsquos urbanization This de facto urban population is the com-mon perhaps only statistic of lsquourban populationrsquo known at the national level in almost allcountries in the world In China however there are multiple series of lsquourban populationrsquoincluding several defined based exclusively on the de jure population counts (see Chan 20072012a)5 The series presented here refers to the number of people counted based on a de factocriterion6

3 Trends and geography of migration since 1982

Based on the figures presented in Table 1 one can identify some broad migration trends Theannual number of hukou migrants recorded by the Ministry of Public Security remained

192 KW Chan

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relatively stable hovering between 17 and 21million in the last three decades In fact the rateof hukou migration as a percentage of Chinarsquos total population has declined significantlyfrom 17 in 1982 to only 13 in 2010 Hukou migrants include mainly rural-rural andrural-urban migrants Systematic information about the composition is not available but it isquite sure that hukou conversion for many migrants remains very stringent in especially largecities (Chan amp Buckingham 2008)

On the other hand the non-hukou migrant population ndash no matter how you measure it ndashhas increased rapidly in the last twenty years For example Series D has expanded from about30million in 1995 to 121million in 2000 and increased by another 100million in the first dec-ade of the 21st C The average annual (net) flow of non-hukou migrant population has been onthe rise since the early 1980s Reflecting the same process the number of those in the urbanareas but with rural hukou increased from 66million in 1990 to 205 million in 2010 ndash a jump145millions in two decades As a percentage of the urban population the number increasedfrom 20ndash23 in the late 1980s and the early 1990s to 31 in 2010 (Series G)

Between 1987 and 1995 the average annual increase in the number of people who stayedin a place different from their hukou registration place was less than 2 million (calculationsbased on Series D) This figure surged to about 10million per year in 2000ndash2010 A similarlyupward trend is seen in the number of rural migrant workers The average increase was aboutabout 6million in 2002ndash2011 compared to an annual average of 5million in the previoustwo decades (based on Series E)7 Furthermore the gap both in absolute and relative termsbetween the de facto urban population and those without rural hukou has widened in the last10ndash15 years suggesting a rather disturbing trend that more and more people in cities andtowns fall into the disadvantaged category lsquoin the city but not of the cityrsquo (Chan 2011) Therapid swelling of the non-hukou population in the city coincides with Chinarsquos ascendency tobeing the lsquoworldrsquos factoryrsquo

As far as the geography is concerned these flows are labor migrations in response to dis-parities in wages between the urban and rural sectors and between regions in China (Cai1999 2000 Fan 2005) The lack of sufficient gainful employment in the countryside inmany agricultural provinces has pushed many rural-hukou workers to leave home They moveto make monetary gains through employment as wage-workers or self-employment Many goto nearby towns outside the villages and most of them move within provinces but about aquarter to one third migrates to big cities on the coast (Chan 2012b)

Drawing on the long-form sample data from Chinarsquos last two censuses and two mini-cen-suses (One Per Cent National Population Surveys) Table 2 provides a summary of the majorinterprovincial migration statistics covering four 5-year periods between 1990 and 2010 forthe population aged 5 and above8 and the aggregate pattern The table permits an examina-tion of the overall pattern of the interprovincial migration flows as well as the changes overtime

As part of the Great Migration interprovincial migration has increased significantly toosince 1990 from only 107 million in 1990ndash1995 to 552 million in 2005ndash20109 Thesecross-province flows are almost totally village-to-city migrations The total interprovincialmigration in those two decades (based on those four 5-year periods) totalled 1363 millionThis number counts more than once of those same interprovincial migrants who moved morethan once in those four periods In 2010 858 million people lived in a place with a hukouregistered in another province compared to only 424 million in 2000 (SC amp NBS 20022012) The doubling of the out-of-province hukou population mirrors the same level ofchange in the overall non-hukou population in the same period (from 121 to 221 million seeD in Table 2)


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12

Table2

InterprovincialMigratio

nin

China19

90ndash201

0(inthou

sand

s)

Provincial-levelunit

1990

ndash1995

1995

ndash2000

2000

ndash2005

2005

ndash2010

1990

ndash2010

Aggregate

Net

migratio

n

aRank

Net

migratio

n

Rank

Net

migratio

n

Rank

In-m

igratio

nOut-m

igratio

nNet

migratio

n

Rank

Net

Rank

Guangdong

1799

196

111063

343

110281

270

113890

1613

12277

222

135420

249

1Zhejiang

273

30

241745

54

34021

106

28407

1339

7067

128

212560

88

2Shanghai

610

66

22005

62

22650

70

34934

401

4533

82

39798

69

3Beijin

g606

66

31715

53

41916

50

53851

406

3445

62

47682

54

4Jiangsu

319

35

5667

21

71963

52

44895

1894

3002

54

55951

42

5Fujian

104

11

10722

22

61132

30

62515

1114

1401

25

63359

24

6Tianjin

171

19

7388

12

8802

21

71499

213

1286

23

72647

19

7Xinjiang

437

48

4925

29

5395

10

8841

287

554

10

82311

16

8Liaoning

248

27

6375

12

9257

07

91181

685

495

09

91375

10

9Hainan

3804

1288

03

1133

01

10339

236

103

02

11262

02

10Nei

Mongol

159

17

81

160

418

23

01

14829

648

181

03

10201

01

11Ningxia

40

1541

01

137

00

11239

151

8902

12141

01

12Tibet

2703

1335

01

146

00

1292

6229

01

1485

01

13Qinghai

1702

144

60

116

12

00

13183

150

3301

138

00

14Yunnan

104

11

9335

10

101

320

315

632

1089

457

08

161

500

115

Shanxi

8709

1149

02

121

350

416

499

794

295

05

152

940

216

Shandong

90

116

2601

151

990

517

1341

2015

674

12

198

560

617

Jilin

134

15

222

750

919

315

08

18345

854

509

09

171

233

09

18Shaanxi

25

03

172

960

920

572

15

21735

1347

613

11

181

506

11

19Gansu

77

08

203

571

121

376

10

19260

1047

787

14

201

597

11

20Hebei

74

08

191

020

317

378

10

20925

2017

1092

20

211

646

12

21Chongqing

NA

NA

NA

655

20

231

010

27

23737

1844

1107

20

222

772

19

22Heilongjiang

188

20

236

392

022

825

22

22323

1463

1140

21

232

792

20

23Guizhou

107

12

219

703

024

1235

32

24592

2681

2088

38

244

400

31

24Guangxi

450

49

261

551

48

251

726

45

25600

2821

2221

40

255

948

42

25Hubei

44

05

181

604

50

262

214

58

27846

3804

2958

54

276

820

48

26Jiangxi

347

38

252

445

76

281

977

52

26699

3483

2784

50

267

553

53

27Hunan

532

58

282

899

90

302

827

74

28690

4592

3902

71

28 1

0160

71

28Henan

514

56

271

839

57

273

154

83

29432

5430

4999

91

311

0506

74

29Anhui

662

72

292

579

80

293

165

83

30824

5526

4702

85

301

1108

78

30Sichuan

1294b

141b

303

806

118

313

178

84

311055

4988

3933

71

291

2211

86

31To

talinterprovincialmigratio

n10661

32330

38042

55228

136261

Notesa asapercentage

ofthenatio

nrsquostotalinterprovincialmigratio

nbincludes

Chongqing

SourcesNPSSO

(199

7)SCamp

NBS(200

220

0720

12)

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Table 2 also shows the ranks of the provinces according to the net migration volumes indifferent periods At this level major in-migration and out-migration flows between provincesare largely unidirectional (Chan 2012b) That means the major players in interprovincialflows were essentially either export provinces (such as Sichuan) or import provinces (such asGuangdong) Because of that the net migration statistics can readily capture the importanceof each province in the interprovincial flows

Table 2 arranges provinces by the aggregate volume of net migration in the two decadeswith Guangdong (the largest net importer) topping the list and Sichuan (the largest net expor-ter) at the bottom The predominant flows are from the inland to the coast In Table 2 thetop seven provinces are all from the coast and the bottom eight from the inland This samepattern is also borne out by the latest census data reporting the flows in 2005ndash2010 as inFigure 2 which maps the largest 20 interprovincial flows in that period

Further examination of Table 2 shows that the net in-migration is dominated by two prov-inces Guangdong and Zhejiang They accounted for 35ndash40 of all interprovincial migrationsince 1995 This dominance is also evident in the latest period as illustrated in Figure 2Guangdong was the most sought-after destination of interprovincial migrants for the entiretwo decades At its peak share in 1995ndash2000 the provincersquos net migration accounted forabout one third of the nationrsquos total interprovincial migration Its share has declined steadilysince 2000 though net migration volume has actually increased The slack has been filled byZhejiang which had a dramatic reversal of role from being a net exporter in 1990ndash1995 tobeing the third largest net importer in 1995ndash2000 and climbed to be the second largestthereafter In the late 20th C the storied Zhejiang migrants were known for their entrepre-neurial skills and ubiquity in the country (and later even in Europe) the high economicgrowth of the province since the mid-1990s has turned it into a major and increasinglypopular destination of migrants in the 21st C

At the sending end there are more plentiful players In 2005ndash2010 there were sevenprovinces with each contributing to 4ndash9 of the total interprovincial migration or more andno single one predominated Sichuan was the largest net exporter of migrants over the twodecades since 1990 (eg in 1990ndash1995 the province outmigration accounted for 1410 ofthe nationrsquos total interprovincial migration) and is no longer so Sichuan dropped to the thirdin net outmigration in 2005ndash2010 In 2000ndash2005 and 2005ndash2010 the share of total migrationfrom the largest four net population exporters (Sichuan Anhui Henan and Hunan) was veryclose (7 to 9) In other words in those 20 years while there was greater convergence ofinterprovincial migration flows into two provinces (Guangdong and Zhejiang) origins ofthese flows became more diverse The changes reflect the intensification of regional industrialrestructuring beginning in the late 1980s whereby inland provinces lost proportionally moremanufacturing jobs to the coastal provinces in the second half of the 1990s and onwards(Yang 2004) giving rise to the emergence of Guangdong (and to a less extent Zhejiang) asthe lsquoworldrsquos factoryrsquo in the last 10 years At the same time many more poor provinces (boththeir governments and people) have actively pursued labor exports as an economic strategymodelled after Sichuan This greater geographic spread of labor migration over longer dis-tances is consistent to what Skeldon (1990) calls the lsquodiffusionrsquo of migration in the case ofPeru

The relative rankings of the provinces over time in Table 2 show remarkable stability in1995 through 2010 Indeed the rankings in 2005ndash2010 are almost exactly the same as thosefor the entire two decades They reflect strongly the economic regional specialization in thatperiod There is a somewhat different picture in the earliest period (1990ndash1995) in Table 2 asdemonstrated by the phenomenal reversal of Zhejiang from a major net exporter in


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1990ndash1995 to a top net importer thereafter Another point worth noting is that there was anoticeable though still small but increasing amount of outmigration from the major netimporters (eg Guangdong) in the first decade of the 21st C often directed towards provincesof origin of the in-migrants such as Hunan Sichuan and Guangxi This outmigration is likelyreturn flows of migrants who came to the coastal cities in the earlier periods

4 Current migrant issues

While the world economy was still mired in recession in 2010 Chinarsquos economy continuedto grow albeit at a slower pace In that year China also overtook Japan to become theworldrsquos second largest economy At present with Europersquos serious debt crisis and the USand Japan struggling to maintain growth many have looked to China as the savior of theworld economy (eg Drysdale 2011) As pointed out before Chinarsquos success in being theworldrsquos factory relies heavily on its own mammoth army of low-cost migrant workers madepossible by its unique hukou system In recent years a series of rather dramatic and signifi-cant events related to migrant labor in China has alerted us to an emerging new reality ndash thatthe ingredients that make the lsquoChina modelrsquo tick may be on the verge of a tectonic change

Figure 2 The 20 largest interprovincial migration flows 2005ndash2010

196 KW Chan

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Moreover the model also looks more fragile than outsiders have portrayed (Chan 2011)These issues have serious implications for both China and for the global economy

41 The global financial crisis and the vulnerability of migrant labor

Occupying the bottom of global supply chain migrant labor was badly hit when the globaleconomy tanked Thousands of factories were closed in Guangdong and Zhejiang as the glo-bal economic crisis unfolded in the open in the last quarter of 2008 and 2009 This led tomassive layoffs of factory workers almost all of whom were migrants without local hukouMany of these factories were folded almost overnight without any warning to their workersand without paying the workers in full and the required severance Guangdongrsquos Dongguanwhich was the core center of Chinarsquos export industry was devastated by the slump Angryworkers staged numerous mass protests demanding full payment of wages and layoff compen-sation Even the international media succeeded in filming and reporting rather unusual scenesof tense confrontations and scuffles with police (Foreman 2008)

Drawing on data from NBS in late March 2009 based mainly on a large national samplesurvey at the end of 2008 Chan (2010d) estimated that the total unemployment of the ruralmigrant labor in early 2009 was about 23million or 164 The sharp loss of 16 of jobsheld by migrants constrasts to only 43 for the urban-hukou workers in formal employmentin March 200911 Indeed more than 95 of the newly laid-off workers in the nonagriculturalsector were rural migrant workers pointing to the extreme vulnerability of this particulargroup Such a situation put China dangerously on the verge of a major crisis (Chan 2010dCsanaacutedi 2010) Fortunately China was able to avert this crisis through implementing agigantic stimulus package and a conciliatory approach to labor disputes by local governments(Csanaacutedi 2010) The massive investment in high-speed train projects for example createdmillions of jobs in 2009 and was able to reemploy some of the laid-off workers fairly quicklyand reverse the situation

42 Shortages of labor and the Lewis turning point (LTP)

As Chinarsquos economy began to recover in the early months of 2010 Chinese factories in afew coastal cities were reported to have difficulties in finding workers to help fill exportorders Some factories in Guangdong resorted to hiring even illegal workers from Vietnamand elsewhere (Sing Tao Daily 2010a) This came as a surprise to many observers becausejust about a year earlier the situation was completely the opposite 23million migrant workerswere laid off as the global financial crisis spread to Chinarsquos manufacturing cities It is alsohard to conceive that severe labor shortages would occur at a time when Chinarsquos working-agepopulation the worldrsquos largest has climbed to a new high (reaching 981million in 2010) andis projected to continue expanding until 2015 (Hu Fang amp Du 2010 Kroeber 2010) Theshortages and the abundance appear to be rather contradictory

Furthermore in May 2010 the world was also shocked by news of a serial tragedyrelated to the treatment of labor in Chinarsquos famed export-processing zones A total of 14ndash16suicide attempts of migrant workers (resulting in 12 deaths) took place in just the first fivemonths of the year in a single giant factory complex Foxconn in Shenzhen the worldrsquos larg-est assembler of electronic products12 for major brand names such as Apple Dell and Toshiba(Moore 2010) The mediarsquos frantic reports have revealed to the world a great deal moreabout the harsh conditions experienced by young Chinese migrant workers13 In the sameweek as Foxconn moved into damage-control mode by offering raises of about 25 to work-


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12

ers multiple serious strikes took place at several Honda assembly plants in the nearby citiesof Foshan and Zhongshan in the Pearl River Delta Those strikes ended after two to threeweeks with the workers winning 10ndash30 raises but they were not the last ones workers atanother Honda subsidiary in Foshan went on strike in July and later (Sing Tao Daily 2010b)

The increasing wages and the militancy of the migrant workers have led both the Chineseand international media in late 2010 to pronounce a labor lsquoshortagersquo and lsquothe end of surpluslaborrsquo in China (eg Demick amp Pierson 2010) More critically some observers have arguedthat China has reached a LTP (Hamlin 2010 Zhang Yang amp Wang 2010)

The LTP hypothesized by Nobel laureate Arthur Lewis (1954) is the point when thedualistic rural-urban labor market in a country begins to break down and merge into one It isalso the point where a labor surplus economy is transformed into a full-employment lsquonormalrsquoeconomy (Huang amp Jiang 2010) According to Lewis a developing countryrsquos industrialwages begin to rise quickly at that point when the supply of surplus labor from its rural areastapers off In the case of China reaching this point would signal that its notorious and tena-cious dualistic rural-urban socioeconomic structure which has existed for the entire six dec-ades of the Peoplersquos Republic era and which is the root cause of a host of social andeconomic ills is beginning to end Reaching the LTP would bring real hopes of closing thehuge rural-urban economic and social chasm in China in the near future And obviously thesignificance of the change would extend far beyond China as the country is the worldrsquos larg-est exporter (Garnaut 2010 Huang amp Jiang 2010)

However it is clear that there is still an abundance not a shortage of surplus rural laborin the country (see eg Yao 2010) This is seemingly a China paradox of migrant laborshortages amidst rural labor supply abundance According to some authors (eg Green 2008Han Cui amp Fan 2009) rural labor supply remains plentiful and the rural sector is stillgrossly overstaffed Rural labor surplus is estimated to be in the order of about 100millionalmost totally in the age group 35 and above at the end of the last decade14 This paradoxcan be explained by reference to Chinarsquos special socioeconomic contexts which are oftenoverlooked in the literature

First because of the lack of local hukou migrant workers are in a weak bargaining posi-tion employers in the export industry are able to lsquocherry-pickrsquo workers with the most lsquodesir-able attributesrsquo ndash often in stark Dickensian terms the physical abilities of young workerssuch as easily trainable dexterity to handle fast-paced sometimes military-style repetitiveassembly work (especially in electronics industry) and endurance for longer hours of work(routine overtime work daily and often for 28ndash29 days per month) and lsquowork attitudesrsquo suchas obedience and capacity for long periods of residence in dormitories or barrack-type shelters(Chan 2001a Lee 1998 Pun 2005) These qualities are mainly found in young mostlyunmarried workers Indeed the great majority of Foxconnrsquos employees in China fit very wellthe above description As a result rural migrant labor hired in the export sector falls over-whelmingly in a highly selective age cohort between ages 16 and 30 with new workers hiredtypically before they reach 20 With their good eyesight and high manual dexterity and toler-ance of dormitory-type environment combined with the increasing prevalence of educationbeyond elementary school in the countryside young rural migrants are more educated thantheir predecessors and better suited for assembling modern electronics which often involvessmall parts and exacting specifications

Secondly Chinarsquos 30-year long one-child policy has also depressed fertility The annualsupply of young rural labor has dropped by about 40 in the last ten years from about 18million to only 11 million (Chan 2010b p 520) Moreover the rather deplorable (mis)treat-ments of migrant labor by employers during the global crisis times in the Pearl River Deltaand Zhejiang also discouraged some of the rural migrants from coming back to seek work in

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the coastal region later even when jobs were available In other words some were simplynot eager to return to the highly exploitative grind of the lsquoworldrsquos factoryrsquo This had the effectof reducing the labor supply that otherwise would have been available for those factories Atthe same time the general improvement in rural economic conditions in the inland region inthe last few years owing to the policy taken by the Hu-Wen administration (Chan amp Wang2008) and especially the millions of new jobs created by the fiscal stimulus package in 2009in projects such as high-speed rails in the interior also kept many young rural laborers closerto home Overall labor shortages have happened in specific age segments and are mostlyconfined to the coastal region There is still an immense rural labor surplus in the range of80ndash100million With dwindling supply of young migrant workers interprovincial flows fromthe inland to the coast may slow down in the coming years

43 The lsquosecond generation migrantsrsquo and social unrest

In recent years rural laborers tend to exit the countryside for the first time in the young adultages much earlier than their predecessors These young migrants are dubbed lsquosecond-genera-tion migrantsrsquo in the literature being born between 1980 and 1995 (Liu Cheng amp Dong2009) According to an authoritative report compiled by Chinarsquos All-China Federation ofTrade Unions (ACFTU 2010) the average age of first departure from the countryside ofthose born in the 1980s and 1990s was 18 and 16 respectively This lsquosecond-generationrsquocohort is the source from which the export industry draws heavily its workforce

Being younger and better educated than their parents this new cohort of migrantworkers has also greater aspirations to stay in the city They are also far more aware oftheir rights and what unsatisfactory conditions they face than the previous generation ndashand are demanding change These demands have often translated into lsquocollective actionrsquoof protests and social unrest which has become more prevalent since 2000 Indeed themigrants have many grievances ranging from unpaid wages unfair treatments and brutal-ity of police against migrants (Hasija 2012) These conflicts are often played out on thebackdrop of stark economic social and institutional divide between migrants and localsHowever the support from family government and social organizations is almost totallylacking Many migrants feel that government officials police and security personnel dis-criminate against them Lacking local hukou migrants cannot access the basic package ofurban services (including education for their children especially beyond middle school)and urban social housing The great difficulty of finding affordable family housing pre-cludes the family moving into the city as one cohesive unit Many married couples areoften separated from spouses and children

In recent years there are hundreds of thousands of protests in China every year Many ofthem are organized by migrants Some recent demonstrations and riots such as those inZengcheng (2011) Chaozhou (2011) and Zhongshan (2012) all in Guangdong have alsoturned violent (Buckley amp Pomfret 2012) It is clear that there is a lot of pent-up angeramong this new generation of migrant workers If these frustrations cannot be diverted theywill pose a serious threat to Chinarsquos social and political stability Worryingly China does notappear to be able to address these grievances satisfactorily under its current labor manage-ment and the hukou systems (Butollo amp ten Brink 2012)

5 Concluding remarks

The volume of internal migration in China has increased steadily since the early 1980s AsChinarsquos urbanized rapidly rural-urban migration also accelerated in the first half of the 1990s


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and again in the first decade of the twenty-first century While the volume of annual hukoumigration remained quite stable in the last 30 years non-hukou migration has expandedsignificantly lsquoRural migrant workersrsquo numbering at 1667million in mid-2012 are the majorconstituent group of non-hukou migrants whose size reached 221 million in 2010 (NBS2012b SC amp NBS 2012) This Great Migration has supplied China with a mammoth armyof low-cost human labor to power its economic machine

Data in Table 1 show that the annual average net increase of the rural migrant laborin latest decade was about 6million compared to an annual average of about 4million inthe 1990s Because of the rapid industrial growth since Chinarsquos accession to the WTOlsquoshortagesrsquo of migrant labor began to surface in 2004 in the Pearl River Delta wheredoubt-digit industrial growth was recorded every year until 200815 Global financial eventssince the summer of 2008 however have drastically altered the economic landscape ofthe Chinese export industry at least temporarily where most migrant labor was employedAbout 23million migrant workers lost their jobs in late 2008-early 2009 but Chinarsquos mas-sive fiscal stimulus program was able to create many jobs (especially in the constructionsector such as building railways) and helped re-absorb most of the unemployed As Chi-narsquos export sector recovered in early 2010 migrant labor lsquoshortagersquo has resurfaced in thecoastal region partly also because major manufacturers have also relocated some of theirplants to the inland However the shortage is limited to the cohort of age 16ndash35 there isenormous surplus labor in the older age in the countryside

Long-distance interprovincial migration has also increased rapidly since the early 1990sspurred by significant wage differentials between inland provinces and coastal provinces wheremajor centers of industrial growth are located Guangdong which has since the early 1990srisen to become the core of the lsquoworldrsquos factoryrsquo is the major hub of long-distance migrantsOver time the number of inland provinces from which large numbers of labor migrants origi-nate has also increased In fact the idea of migrating long-distance for a better job has gainedpopularity over time in many provinces including those in the West region of China The ris-ing internal migration trends in the 1990s are also associated with the trends of narrowing eco-nomic disparities among provinces at least in statistical terms if not in substantive terms(Chan amp Wang 2008)16 Migration has helped alleviated poverty in the countryside but sim-ply migration is not enough when institutionalized exclusion and discrimination through thehukou system remains effective

The hukou system is a major hurdle in narrowing rural-urban and inland-coastal inequali-ties The various hukou reform initiatives launched in the last decades have so far onlymarginally weakened the foundation of that exclusionary system ie the separation of twosegments of the population and the discrimination against the rural segment (Chan ampBuckingham 2008) Greater strides on implementing the hukou reform and ultimately abol-ishing the system are needed This is especially urgent as more women and children fromthe countryside participate in migration to cities and are staying for increasingly long periodsof time Non-hukou residents in cities face acute problems caused by the lack of access toreasonable education health care services social security and housing in addition to the gen-eral discrimination they face in the city

Several recent events ndash the serial suicide attempts of migrant workers at Foxconn inShenzhen industrial strikes by migrant workers in several automobile plants and frequentprotest and riots involving migrants ndash have not only highlighted the plight of young lsquosecond-generationsrsquo migrant workers but are also strong signs of rising yearning of migrants to fightfor better pays and rights These migrant issues remain crucial for China to tackle in the post-financial crisis era Further delays in responding to these demands would be far more costlyThose events also suggest that the low lsquoChina pricersquo that the world has been accustomed to is

200 KW Chan

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217

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Dec

embe

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belatedly rising If lsquoMade in Chinarsquo is getting more expensive the global economy will haveto take note too

Notes on contributorKam Wing Chan is Professor in Geography at the University of Washington His research focuses onChinarsquos migrant labor and urbanization

Notes1 The description of Chinarsquos hukou system draws heavily on Chan (2009a) and Chan (2010a)2 Some of these data were collected at the destination others at the origin (mainly villages)3 A broader definition of lsquorural migrant laborrsquo which is not used here includes also those who work

in township and village enterprises within the same township This group was estimated to be about80 million in 2009 (NBS 2010)

4 They are similar to the lsquocirculating populationrsquo studied by Chapman amp Prothero (1985) before5 This has led to multiple layers of misunderstanding and confusion in the literature see especially

Chan (2007 2010c) Chan and Wang (2008)6 In the last two censuses (2000 and 2010) de facto resident counts include those without local hukou

but staying at the destination for more than 6 months7 There was a slowdown in the rural outflows in the second half of the 1990s See Chan and Hu

(2003)8 The 1990ndash2005 statistics are compiled by Chan (2012a) the 2005ndash2010 statistics are estimates

computed from the 2010 Census long-form data in SC amp NBS (2012 Tables 7 and 8) based on thesample percentage (10) The accuracy of Chinarsquos population and migration counts is a rather com-plex issue deserving further research On this issue pertaining to 2000 Census see Chan (2003)

9 Some of the increases are attributable to the changes in definitions and procedures used for collect-ing the data but the bulk of the increase is real

10 This was partly because Chongqing was split from the Sichuan province in 199711 For the urban-hukou group the unemployment rate only inched up by 06 million from the 40 in

September 2008 (Cai amp Chan 2009 Chan 2010d)12 Foxconn employed about 800000 workers in China in 2010 with 400000 at the complex in

Shenzhen (Barboza 2010)13 Foxconn is known for its military-style efficiency and strict rule-based management of labor

(Barboza 2010) See also a detailed investigative report about the factory by Chang (2010)14 Chinarsquos rural labor force is estimated to be about 360 million in 2010 Excluding the 80 million

who work in non-farm jobs in nearby townships the remaining 280 million on the farm is still fargreater than can be absorbed by Chinarsquos 120 million ha of arable land Many estimates done before2009 show that the minimum work force needed to sustain Chinarsquos agriculture at the then level oftechnology was about 150 million (eg Green 2008 Han et al 2009) Higher numbers are used byothers such as Cai and Wang (2009 Table 72) they range from 178 million to 228 million Forcomparison the US employs about 10 million farm workers (illegal migrant workers included) on atotal acreage slightly more than Chinarsquos (see DeSilver 2006)

15 In Dongguan in the Pearl River Delta the average growth rate of migrant labor reached an extraor-dinary level of 18 per year in 2000ndash2004 (Dongguan Statistical Bureau nd)

16 See further comments at httpwwweastasiaforumorg20120812rising-regional-inequality-in-china-fact-or-artefact

ReferencesAll-China Federation of Trade Unions (ACFTU) (2010) Quanzong guanyu xinshengdai nongmingong

wenti de yanjiu baogao [Research report on the problems of the new generation of rural migrantlabor] Retrieved from httpwwwchinanewscomcngnnews201006ndash212353235shtml

Barboza D (2010 May 26) Electronics maker promises review after suicides The New York TimesRetrieved from httpwwwnytimescom20100527technology27suicidehtml

Buckley C amp Pomfret J (2012 July 7) China migrant unrest exposes generational faultline ReutersJune 29 2012 Retrieved from httpinreuterscomarticle20110629idINIndia-57978320110629


Dow

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ded

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Kam

Win

g C

han]

at 1

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30

Dec

embe

r 20

12

Butollo F amp ten Brink T (2012) Challenging the atomization of discontent Patterns of migrant-worker protest in China during the series of strikes in 2010 Critical Asian Studies 44(3) 419ndash440

Cai F (1999) Spatial patterns of migration under Chinarsquos reform period Asian and Pacific MigrationJournal 8(3) 313ndash327

Cai F (2000) Zhongguo liudong renkou [Floating Population in China] Zhengzhou Henan RenminChubanshe

Cai F amp Chan K W (2009) The global economic crisis and unemployment in China EurasianGeography and Economics 50 513ndash531

Cai F amp Wang M (2009) The counterfactuals of unlimited surplus labor in rural China In F Cai ampY Du (Eds) The China population and labor yearbook volume 1 The approaching lewis turningpoint and its policy implications (pp 121ndash136) Leiden Brill

Chan KW (1994) Cities with invisible walls Reinterpreting urbanization in post-1949 China HongKong Oxford University Press

Chan A (2001a) Chinarsquos workers under assault The exploitation of labour in a globalizing economyArmonk NY ME Sharpe

Chan K W (2001b) Recent migration in China Patterns trends and policies Asian Perspectives 25(4) 127ndash155

Chan K W (2003) Chinese census 2000 New opportunities and challenges The China Review 3(2)1ndash12

Chan K W (2007) Misconceptions and complexities in the study of Chinarsquos cities Definitions statis-tics and implications Eurasian Geography and Economics 48(4) 382ndash412

Chan K W (2009a) The Chinese hukou system at 50 Eurasian Geography and Economics 50(2)197ndash221

Chan K W (2009b) Measuring the urban millions China Economic Quarterly March 21ndash26Chan K W (2010a) The Chinese household registration system and migrant labor in China Notes on

a debate Population and Development Review 36(2) 357ndash364Chan K W (2010b) A China paradox migrant labor shortage amidst rural labor supply abundance

Eurasian Geography and Economics 51 513ndash530Chan K W (2010c) Dangqian zhongguo de chengzhen renkou tongji wenti jiqi du jingji fenxi de

yingxiang [Chinarsquos city population statistics and implications for economic analysis] In F Cai YDu amp Z Zhang (Eds) Zhongguo renkou yu laodong wenti baogao No11 ndash Hou jinrong weiji shiqide laodongli shichang tiaozhan [Reports on Chinarsquos population and labor No 11 ndash Labor marketchallenges in the post-crisis era] (pp 236ndash247) Beijing Social Science Academic Press

Chan K W (2010d) The global financial crisis and migrant workers in China There is no future as alabourer returning to the village has no meaning International Journal of Urban and RegionalResearch 34(3) 659ndash677

Chan K W (2011) Urban myth South China Morning Post August 24 p A13Chan K W (2012a) Crossing the 50 percent population Rubicon Can China urbanize to prosperity

Eurasian Geography and Economics 53(1) 63ndash86Chan K W (2012b) Internal labor migration in China Trends geography and policies In United

Nations Population Division Population distribution urbanization internal migration and develop-ment An international perspective (pp 81ndash102) New York NY United Nations

Chan K W amp Buckingham W (2008) Is China abolishing the hukou system China Quarterly 195582ndash606

Chan K W amp Hu Y (2003) Urbanization in China in the 1990s New definition different series andrevised trends The China Review 3(2) 49ndash71

Chan K W Liu T amp Yang Y (1999) Hukou and non-hukou migration Comparisons and contrastsInternational Journal of Population Geography 5(6) 425ndash448

Chapman M amp Prothero R M (Eds) (1985) Circulation in population movement Substance andconcepts from the Melanesian case London Routlege amp Kegan Paul

Chan K W amp Wang M (2008) Remapping Chinarsquos regional inequalities 1990ndash2006 A new assess-ment of de facto and de jure population data Eurasian Geography and Economics 49(1) 21ndash56

Chang C (2010 May 20) Undercover report from Foxconnrsquos hell factory Gizmodo AustraliaRetrieved from httpwwwgizmodocomau201005undercover-report-from-foxconns-hell-factory

Csanaacutedi M (2010) Institutional reactions to the impact of global crisis at source and destination citiesof migration in China Budapest Hungary Institute of Economics Hungarian Academy of SciencesDiscussion Paper MT-DP 201013

202 KW Chan

Dow

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ded

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Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12

Demick B amp Pierson D (2010 March 28) People people everywhere in China and not enough towork Los Angeles Times Retrieved from httparticleslatimescom2010mar28worldla-fgchina-labor28-2010mar28

DeSilver D (2006) Low-paid illegal work force has little impact on prices Seattle Times September19 2006 Retrieved from httpseattletimesnwsourcecomhtmllocalnews2003265139_imprices19html

Dongguan Statistical Bureau (nd) Retrieved from httptjjdggovcnwebsitewebzhctjnj2005TJNJ02sheet004htm

Drysdale P (2011 November 28) South Asia and Asiarsquos middle-class future East Asia ForumRetrieved from httpwwweastasiaforumorg20111128south-asia-and-asias-middle-class-future

Fan C C (2005) Modelling interprovincial migration in China 1985ndash2000 Eurasian Geography andEconomics 46(3) 165ndash184

Fan C C (2008) China on the move Migration the state and the household New York NY RoutledgeForeman W (2008 December 20) Restless migrants challenge order in difficult economy Post-Intelli-

gencerGarnaut R (2010 April 6) Macro-economic implications of the turning point Paper presented at Inter-

national Workshop on Debating the Lewis Turning Point in China Beijing ChinaGreen S (2008 January 14) On the worldrsquos factory floor How Chinarsquos workers are changing China

and the global economy Standard Chartered Special ReportHamlin K (2010) China reaching a Lewis turning point as inflation overtakes low-cost labor Bloom-

berg News Retrieved from httpwwwbloombergcomappsnewspid=20601068ampsid=aOEXbd09bloM

Han J Cui C amp Fan A (2009) Rural labor-force allocation report-an investigation of 2749 villagesIn F Cai amp Y Du (Eds) The China population and labor yearbook volume 1 The approachingLewis turning point and its policy implications (pp 137ndash152) Leiden Brill

Harney A (2008) The China price The true cost of Chinese competitive advantage London PenguinBooks

Hasija N (2012 Jan 17) Migrant unrest in China An analysis (IPCS Special Report 119) New DelhiHu Y Fang C amp Du Y (2010) Shierwu shiqi renkou bianhua ji weilai renkou fazhan qushi yuce

[Population changes and forecast of population development trend in lsquothe Twelfth Five-Year-Planrsquoperiod] In F Cai (Ed) Zhongguo renkou yu laodong wenti baogao No11 ndash houjinrong weiji shiqide laodongli shichang tiaozhan [Report on Chinese population and labor problem No11 ndash Chal-lenge of the labor market in the post-financial crisis period] (pp 48ndash77) Beijing Shehui kexuewenxian chubanshe

Huang Y amp Jiang T (2010) What does the Lewis turning point mean for China Beijing China Cen-ter for Economic Research Peking University Working Paper Series 2010-03

Kroeber A (2010) The end of surplus labor China Economic Quarterly 1 35ndash46Lee C K (1998) Gender and the south China miracle Berkeley University of California PressLewis W A (1954) Economic development with unlimited supplies of labor Manchester School of

Economic and Social Studies 22 139ndash191Liang Z (1999) Foreign investment economic growth and temporary migration The case of Shenzhen

Special Economic Zone China Development and Society 28(1) 115ndash137Liang Z (2007) Internal migration Policy changes recent trends and new challenges In Z Zhao amp

F Guo (Eds) Transition and challenge Chinarsquos population at the beginning of the 21st century(pp 197ndash215) Oxford Oxford University Press

Liu T amp Chan K W (2001) Internal migration in China and its database An assessment ChinaInformation 15(2) 75ndash113

Liu C Cheng J amp Dong Y (2009) Zhongguo dierdai nongmingong yanjiu [Research on Chinarsquos sec-ond-generation rural migrant labor] Shangdong renmin chubanshe

Lu M Zhao S amp Bai N (2002) Woguo nongmingong laodongli liudong di huigu yu yuce [The pastand future of the movement of the rural labor force in China] In M Hong amp W Mengkui (Eds)Zhongguo fazhan yanjiu [China development studies] (pp 555ndash587) Beijing Zhongguo chubanshe

Ma J (2012) National economy maintained steady and fast development in the year of 2011 httpwwwstatsgovcnenglishnewsandcomingeventst20120117_402779577htm

Ministry of Public Security (MPS) (1988ndash2010) Zhonghua renmin gongheguo quanguo fenxianshi ren-kou tongji ziliao [Statistical materials on population of counties and cities of the Peoplersquos Republicof China] Beijing Qunzhong


Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12

Ministry of Public Security (MPS) Household Administration Bureau (1997ndash2011) Quanguo zanzhurenkou tongti ziliao huibian [Collection of statistical materials on temporary population in China]Beijing Zhongguo gongan daxue chubanshe

Moore M (2010 May 27) Inside Foxconnrsquos suicide factory Telegraph Retrieved from httpwwwtele-graph coukfinancechina-business7773011A-look-inside-the-Foxconn-suicide-factoryhtml

National Bureau of Statistics (NBS) (1988) Zhongguo 1987 nian 1 per cent renkou chouyan diaochaziliao [Tabulations of Chinarsquos 1987 1 per cent Population Sample Survey] Beijing Zhongguo tongjichubanshe

National Bureau of Statistics (NBS) (2010) 2009 nian nongmingong jiance baogao [Monitor report onrural migrant labor in 2009] Retrieved from httpwwwstatsgovcntjfxfxbgt20100319_402628281htm

National Bureau of Statistics (NBS) (2011) 2010 nian nongmingong jiance baogao [Monitor report ofrural migrant labor in 2010] Retrieved from httpwwwsnzgcomcnReadNewsaspNewsID=3936

National Bureau of Statistics (NBS) (2012a) 2011 nian nongmingong jiance baogao [Monitor reporton rural migrant labor in 2011] Retrieved from httpwwwstatsgovcntjfxfxbgt20120427_402801903htm

National Bureau of Statistics (NBS) (2012b July 13) Overall economic development was stable in thefirst half of 2012 Retrieved from httpwwwstatsgovcnenglishpressreleaset20120713_402817907htm

National Bureau of Statistics amp Ministry of Public Security (NBS amp MPS) (1988) Zhonghua renmingongheguo renkou tongji ziliao huibian [Collections of Statistical Materials on Population of thePeoplersquos Republic of China] Beijing Zhongguo caizheng jingji chubanshe

National Population Sample Survey Office (NPSSO) (1997) 1995 Quanguo 1 percent renkou chouyangdiaocha ziliao [Data on the Sample Survey of 1 percent of the National Population in 1995] Bei-jing Zhongguo tongji chubanshe

National Population Family Planning Commission (NPFPC) (2011) Zhongguo liudong renkou fazhanbaogao 2011 [Report on Chinarsquos Floating Population Development 2011] Beijing Zhongguo ren-kou chubanshe


Pun N (2005) Made in China Women factory workers in a global workplace London Duke Univer-sity Press and Hong Kong University Press

Saunders D (2012) Arrival city How the largest migration in history is reshaping our world NewYork NY Vintage Books

Sing Tao Daily (2010a July 3) Yuexin shuizhang waiji heigong yongru [Wage Raises in GuangzhouLed to Influx of Foreign Illegal Labor] p A11

Sing Tao Daily (2010b July 16) Foshan bentian qipeichang caibao gongchao (Strike Broke Out Againin Honda Subsidiary in Foshan) p A10

Skeldon R (1990) Population mobility in developing countries London BelhavenState Council amp National Bureau of Statistics (SC amp NBS) (1985) Zhongguo 1982 nian renkou pucha

ziliao [Tabulation on the 1982 Population Census of the Peoplersquos Republic of China] BeijingZhongguo tongji chubanshe

State Council amp National Bureau of Statistics (SC amp NBS) (1993) Zhongguo 1990 nian renkou puchaziliao [Tabulation on the 1990 Population Census of the Peoplersquos Republic of China] BeijingZhongguo tongji chubanshe

Solinger D (1999) Contesting citizenship in Urban China Berkeley University of California PressState Council amp National Bureau of Statistics (SC amp NBS) (2002) Zhongguo 2000 nian renkou pucha


State Council amp National Bureau of Statistics (SC amp NBS) (2007) 2005 nian quanguo 1 percent ren-kou chouyang diaocha ziliao [Data on the Sample Survey of 1 percent of the National Population in2005] Beijing Zhongguo tongji chubanshe


Tilly C (1976) Migration in modern history In W H McNeill amp R S Adams (Eds) Human migra-tion Patterns and policies (pp 48ndash72) Bloomington Indiana University Press

204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12

United Nations (2012 August 3) International migration and development Report of the Secretary-General

Wang F-L (2005) Organizing through division and exclusion Stanford Stanford University PressWu J-M (2011) Yongyuan de yixiangke Gongmin shenfen chaxu yu zhongguo nongmingong jieji

[Strangers forever Differential citizenship and Chinarsquos rural migrant workers] Taiwanese Sociology21 51ndash99

Yang Y (1996) lsquoTemporary residentsrsquo in China Causes and characteristics Chinese Environment andDevelopment 7(1ndash2) 103ndash117

Yang Y (2004) Jiushi niandai yilai woguo renkou qianyi de ruogan xin tedian [New features of popu-lation migration in the 1990s in China] Nanfang renkou [Southern Population] 75 13ndash20

Yao Y (2010 July 16) The Lewisian turning point has not yet arrived The Economist httpwwweconomistcomeconomicsby-invitationquestionsera_cheap_chinese_labour_over

Zhang P (2012 September 7) Exam rules anger migrant families South China Morning Post pA6Zhang X Yang J amp Wang S (2010) China has reached the Lewis turning point Washington DC

International Food and Policy Research Institute (Discussion Paper 000977)


Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12

Migration and development in China trends geography and currentissues

Kam Wing Chan

Department of Geography University of Washington Seattle WA USA

(Received 13 August 2012 final version received 14 September 2012)

Chinarsquos recent meteoric rise in the global economy is closely related to the strength of itsmanufacturing sector which is heavily dependent on cheap migrant labor This paper ana-lyzes Chinarsquos recent migration trends spatial pattern and their relationship with Chinarsquoseconomic strategy Internal migration in China is special in that it is heavily controlled andregulated by the hukou (household registration) system The system enables the country tocreate a massive exploitable migrant labor force that makes Chinarsquos industry highly com-petitive in the global economy The paper explains how the system works and distils thecomplex population and migration statistics to present a relatively complete picture ofmigration over time and space including the latest changes Special focus is on analyzinglsquorural migrant laborrsquo which has constituted the most important human cog powering theChina economic engine Long-distance interprovincial migration is also studied in the con-text of the changes in the regional economy in supporting Chinarsquos ascendancy to being thelsquoworldrsquos factoryrsquo Three major issues pertaining to this migrant labor system and recentdevelopments in China and the global economy are examined They presage importantchanges to come which are likely to impact both China and the rest of the world

Keywords China migration hukou system interprovincial migration worldrsquos factory

1 Introduction

Chinarsquos rise as the lsquoworldrsquos factoryrsquo is definitely one of the biggest stories of the 21st CChinarsquos dominance in manufacturing has made it a major player in the global economy ThisChina success story is closely intertwined with the migration story without the epic-scalemigration of peasants ndash which supplies almost infinite low-cost human labor to power theChina economic engine ndash the ascent of China would be totally unthinkable Cheap migrantlabor is what makes lsquoChina pricersquo so unrelenting (Harney 2008) The last three decades havewitnessed the worldrsquos lsquoGreat Migrationrsquo ndash an estimated 200ndash250 million rural residents havemoved to cities and towns within China (Chan 2012a) To put this in perspective the volumeof the Great Migration of Europeans to North America from 1800 to the World War I wasonly a fraction of Chinarsquos lsquoon the order of fifty million personsrsquo (Tilly 1976 p 58)

Building on the previous work (Chan 2001b 2012b Liang 2007) and using the latestCensus data available this paper analyzes Chinarsquos recent migration trends spatial pattern andtheir relationship with economic development Chinarsquos internal migration is special in that itis heavily controlled and regulated by the hukou (household registration) system (Chan 1994Fan 2008) As will be explained below through the special institutional design of the hukou

Email kwchanuwedu

Migration and DevelopmentVol 1 No 2 December 2012 187ndash205

ISSN 2163-2324 printISSN 2163-2332 online 2012 Taylor amp Francishttpdxdoiorg101080216323242012739316httpwwwtandfonlinecom

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






188 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12

Table

1Major

aggregatemigratio

nandurbanpo

pulatio

nstatistics19


HukouMigrants

(Yearlyflow

figures)

Non

-HukouPop

ulation

(Stock

figures)

Defactopopulatio

nin

urbanareas

lsquoTem

porary

Populationrsquo

lsquoRural

Migrant

Laborrsquo

Rural-hukou

populatio

nin

urbanareas

Policestatistics

Registeredwith

MPS(m

id-year)


NBSestim

ates

basedon

samplesurveys

Authorrsquosestim

ates

NBSstatistics

Geographicboundary

(tocross)

Citytownor

township

Township-

levelunit

Countyor

city

Tow

nship

Not

applicable

Not

applicable

Minim

umlength

ofstay

(for

those

with

outlocalhukou)

Nominim

um3days

6months

6months(unless

otherw

isespecified)

Away

foroutsidework

(for

6monthsor

more)

6months

6months

Series

AB

CD

EF

G(

)H

1982

1730

66(1year)

465

217

2145

1987

1973

152

a260

c640

231

2767

1990

1924

216

(1year)

663

220

3020

1995

1846

497

291

b750

694

197

3517

2000

1908

448

1444

1210

1366

298

4591

2001

1701

551

1486

309

4806

2002

1722

598

1047

1528

304

5021

2003

1726

699

1139

1495

285

5238

2004

1949

780

1182

1514

279

5428

2005

1933

867

1531

1258

1531

272

5621

2006

2060

953

1321

1564

271

5771

2007

2084

1044

1370

1630

275

5938

2008

1892

1166

1404

1670

275

6067

2009

1677

1222

1453

1716

276

6219

2010

1701

1314

2614

2214

1534

2056

309

6656

2011

1554

1586

6908

Notes

andsources

MPS=Ministryof

Public

Security


Statistics

a the

geographic

boundary

isbasedon

citycounty

ortown

bthegeographic

boundary

isbasedon

county-level

units

c refersto

1988

AMPS(1988ndash

2010)NBSamp


2011)C

andDNBS(1988)SC

ampNBS(1985

1993200220072012)andNPSSO

(1997)E2002


NBS

compliedby

Cai

andChan(2009

Table1)2009


NBS(2010

2011


ates

inLuet

al(2002)

andthey

may

notbe

fully

comparableto

recent

NBSfiguresFEstim

ates

derivedfrom


apercentage


Chan(2012a)2011

figure

isfrom

Ma(2012)

190 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12












Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12








192 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12








Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12

Table2


nin

China19

90ndash201

0(inthou

sand

s)


1990

ndash1995

1995

ndash2000

2000

ndash2005

2005

ndash2010

1990

ndash2010

Aggregate

Net

migratio

n

aRank

Net

migratio

n

Rank

Net

migratio

n

Rank

In-m

igratio

nOut-m

igratio

nNet

migratio

n

Rank

Net

Rank

Guangdong

1799

196

111063

343

110281

270

113890

1613

12277

222

135420

249

1Zhejiang

273

30

241745

54

34021

106

28407

1339

7067

128

212560

88

2Shanghai

610

66

22005

62

22650

70

34934

401

4533

82

39798

69

3Beijin

g606

66

31715

53

41916

50

53851

406

3445

62

47682

54

4Jiangsu

319

35

5667

21

71963

52

44895

1894

3002

54

55951

42

5Fujian

104

11

10722

22

61132

30

62515

1114

1401

25

63359

24

6Tianjin

171

19

7388

12

8802

21

71499

213

1286

23

72647

19

7Xinjiang

437

48

4925

29

5395

10

8841

287

554

10

82311

16

8Liaoning

248

27

6375

12

9257

07

91181

685

495

09

91375

10

9Hainan

3804

1288

03

1133

01

10339

236

103

02

11262

02

10Nei

Mongol

159

17

81

160

418

23

01

14829

648

181

03

10201

01

11Ningxia

40

1541

01

137

00

11239

151

8902

12141

01

12Tibet

2703

1335

01

146

00

1292

6229

01

1485

01

13Qinghai

1702

144

60

116

12

00

13183

150

3301

138

00

14Yunnan

104

11

9335

10

101

320

315

632

1089

457

08

161

500

115

Shanxi

8709

1149

02

121

350

416

499

794

295

05

152

940

216

Shandong

90

116

2601

151

990

517

1341

2015

674

12

198

560

617

Jilin

134

15

222

750

919

315

08

18345

854

509

09

171

233

09

18Shaanxi

25

03

172

960

920

572

15

21735

1347

613

11

181

506

11

19Gansu

77

08

203

571

121

376

10

19260

1047

787

14

201

597

11

20Hebei

74

08

191

020

317

378

10

20925

2017

1092

20

211

646

12

21Chongqing

NA

NA

NA

655

20

231

010

27

23737

1844

1107

20

222

772

19

22Heilongjiang

188

20

236

392

022

825

22

22323

1463

1140

21

232

792

20

23Guizhou

107

12

219

703

024

1235

32

24592

2681

2088

38

244

400

31

24Guangxi

450

49

261

551

48

251

726

45

25600

2821

2221

40

255

948

42

25Hubei

44

05

181

604

50

262

214

58

27846

3804

2958

54

276

820

48

26Jiangxi

347

38

252

445

76

281

977

52

26699

3483

2784

50

267

553

53

27Hunan

532

58

282

899

90

302

827

74

28690

4592

3902

71

28 1

0160

71

28Henan

514

56

271

839

57

273

154

83

29432

5430

4999

91

311

0506

74

29Anhui

662

72

292

579

80

293

165

83

30824

5526

4702

85

301

1108

78

30Sichuan

1294b

141b

303

806

118

313

178

84

311055

4988

3933

71

291

2211

86

31To


n10661

32330

38042

55228

136261


ofthenatio


nbincludes

Chongqing

SourcesNPSSO

(199

7)SCamp

NBS(200

220

0720

12)

194 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12





196 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







198 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






200 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12



























202 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12


























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






188 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12

Table

1Major

aggregatemigratio

nandurbanpo

pulatio

nstatistics19


HukouMigrants

(Yearlyflow

figures)

Non

-HukouPop

ulation

(Stock

figures)

Defactopopulatio

nin

urbanareas

lsquoTem

porary

Populationrsquo

lsquoRural

Migrant

Laborrsquo

Rural-hukou

populatio

nin

urbanareas

Policestatistics

Registeredwith

MPS(m

id-year)


NBSestim

ates

basedon

samplesurveys

Authorrsquosestim

ates

NBSstatistics

Geographicboundary

(tocross)

Citytownor

township

Township-

levelunit

Countyor

city

Tow

nship

Not

applicable

Not

applicable

Minim

umlength

ofstay

(for

those

with

outlocalhukou)

Nominim

um3days

6months

6months(unless

otherw

isespecified)

Away

foroutsidework

(for

6monthsor

more)

6months

6months

Series

AB

CD

EF

G(

)H

1982

1730

66(1year)

465

217

2145

1987

1973

152

a260

c640

231

2767

1990

1924

216

(1year)

663

220

3020

1995

1846

497

291

b750

694

197

3517

2000

1908

448

1444

1210

1366

298

4591

2001

1701

551

1486

309

4806

2002

1722

598

1047

1528

304

5021

2003

1726

699

1139

1495

285

5238

2004

1949

780

1182

1514

279

5428

2005

1933

867

1531

1258

1531

272

5621

2006

2060

953

1321

1564

271

5771

2007

2084

1044

1370

1630

275

5938

2008

1892

1166

1404

1670

275

6067

2009

1677

1222

1453

1716

276

6219

2010

1701

1314

2614

2214

1534

2056

309

6656

2011

1554

1586

6908

Notes

andsources

MPS=Ministryof

Public

Security


Statistics

a the

geographic

boundary

isbasedon

citycounty

ortown

bthegeographic

boundary

isbasedon

county-level

units

c refersto

1988

AMPS(1988ndash

2010)NBSamp


2011)C

andDNBS(1988)SC

ampNBS(1985

1993200220072012)andNPSSO

(1997)E2002


NBS

compliedby

Cai

andChan(2009

Table1)2009


NBS(2010

2011


ates

inLuet

al(2002)

andthey

may

notbe

fully

comparableto

recent

NBSfiguresFEstim

ates

derivedfrom


apercentage


Chan(2012a)2011

figure

isfrom

Ma(2012)

190 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12












Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12








192 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12








Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12

Table2


nin

China19

90ndash201

0(inthou

sand

s)


1990

ndash1995

1995

ndash2000

2000

ndash2005

2005

ndash2010

1990

ndash2010

Aggregate

Net

migratio

n

aRank

Net

migratio

n

Rank

Net

migratio

n

Rank

In-m

igratio

nOut-m

igratio

nNet

migratio

n

Rank

Net

Rank

Guangdong

1799

196

111063

343

110281

270

113890

1613

12277

222

135420

249

1Zhejiang

273

30

241745

54

34021

106

28407

1339

7067

128

212560

88

2Shanghai

610

66

22005

62

22650

70

34934

401

4533

82

39798

69

3Beijin

g606

66

31715

53

41916

50

53851

406

3445

62

47682

54

4Jiangsu

319

35

5667

21

71963

52

44895

1894

3002

54

55951

42

5Fujian

104

11

10722

22

61132

30

62515

1114

1401

25

63359

24

6Tianjin

171

19

7388

12

8802

21

71499

213

1286

23

72647

19

7Xinjiang

437

48

4925

29

5395

10

8841

287

554

10

82311

16

8Liaoning

248

27

6375

12

9257

07

91181

685

495

09

91375

10

9Hainan

3804

1288

03

1133

01

10339

236

103

02

11262

02

10Nei

Mongol

159

17

81

160

418

23

01

14829

648

181

03

10201

01

11Ningxia

40

1541

01

137

00

11239

151

8902

12141

01

12Tibet

2703

1335

01

146

00

1292

6229

01

1485

01

13Qinghai

1702

144

60

116

12

00

13183

150

3301

138

00

14Yunnan

104

11

9335

10

101

320

315

632

1089

457

08

161

500

115

Shanxi

8709

1149

02

121

350

416

499

794

295

05

152

940

216

Shandong

90

116

2601

151

990

517

1341

2015

674

12

198

560

617

Jilin

134

15

222

750

919

315

08

18345

854

509

09

171

233

09

18Shaanxi

25

03

172

960

920

572

15

21735

1347

613

11

181

506

11

19Gansu

77

08

203

571

121

376

10

19260

1047

787

14

201

597

11

20Hebei

74

08

191

020

317

378

10

20925

2017

1092

20

211

646

12

21Chongqing

NA

NA

NA

655

20

231

010

27

23737

1844

1107

20

222

772

19

22Heilongjiang

188

20

236

392

022

825

22

22323

1463

1140

21

232

792

20

23Guizhou

107

12

219

703

024

1235

32

24592

2681

2088

38

244

400

31

24Guangxi

450

49

261

551

48

251

726

45

25600

2821

2221

40

255

948

42

25Hubei

44

05

181

604

50

262

214

58

27846

3804

2958

54

276

820

48

26Jiangxi

347

38

252

445

76

281

977

52

26699

3483

2784

50

267

553

53

27Hunan

532

58

282

899

90

302

827

74

28690

4592

3902

71

28 1

0160

71

28Henan

514

56

271

839

57

273

154

83

29432

5430

4999

91

311

0506

74

29Anhui

662

72

292

579

80

293

165

83

30824

5526

4702

85

301

1108

78

30Sichuan

1294b

141b

303

806

118

313

178

84

311055

4988

3933

71

291

2211

86

31To


n10661

32330

38042

55228

136261


ofthenatio


nbincludes

Chongqing

SourcesNPSSO

(199

7)SCamp

NBS(200

220

0720

12)

194 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12





196 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







198 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






200 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12



























202 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12


























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12

Table

1Major

aggregatemigratio

nandurbanpo

pulatio

nstatistics19


HukouMigrants

(Yearlyflow

figures)

Non

-HukouPop

ulation

(Stock

figures)

Defactopopulatio

nin

urbanareas

lsquoTem

porary

Populationrsquo

lsquoRural

Migrant

Laborrsquo

Rural-hukou

populatio

nin

urbanareas

Policestatistics

Registeredwith

MPS(m

id-year)


NBSestim

ates

basedon

samplesurveys

Authorrsquosestim

ates

NBSstatistics

Geographicboundary

(tocross)

Citytownor

township

Township-

levelunit

Countyor

city

Tow

nship

Not

applicable

Not

applicable

Minim

umlength

ofstay

(for

those

with

outlocalhukou)

Nominim

um3days

6months

6months(unless

otherw

isespecified)

Away

foroutsidework

(for

6monthsor

more)

6months

6months

Series

AB

CD

EF

G(

)H

1982

1730

66(1year)

465

217

2145

1987

1973

152

a260

c640

231

2767

1990

1924

216

(1year)

663

220

3020

1995

1846

497

291

b750

694

197

3517

2000

1908

448

1444

1210

1366

298

4591

2001

1701

551

1486

309

4806

2002

1722

598

1047

1528

304

5021

2003

1726

699

1139

1495

285

5238

2004

1949

780

1182

1514

279

5428

2005

1933

867

1531

1258

1531

272

5621

2006

2060

953

1321

1564

271

5771

2007

2084

1044

1370

1630

275

5938

2008

1892

1166

1404

1670

275

6067

2009

1677

1222

1453

1716

276

6219

2010

1701

1314

2614

2214

1534

2056

309

6656

2011

1554

1586

6908

Notes

andsources

MPS=Ministryof

Public

Security


Statistics

a the

geographic

boundary

isbasedon

citycounty

ortown

bthegeographic

boundary

isbasedon

county-level

units

c refersto

1988

AMPS(1988ndash

2010)NBSamp


2011)C

andDNBS(1988)SC

ampNBS(1985

1993200220072012)andNPSSO

(1997)E2002


NBS

compliedby

Cai

andChan(2009

Table1)2009


NBS(2010

2011


ates

inLuet

al(2002)

andthey

may

notbe

fully

comparableto

recent

NBSfiguresFEstim

ates

derivedfrom


apercentage


Chan(2012a)2011

figure

isfrom

Ma(2012)

190 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12












Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12








192 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12








Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12

Table2


nin

China19

90ndash201

0(inthou

sand

s)


1990

ndash1995

1995

ndash2000

2000

ndash2005

2005

ndash2010

1990

ndash2010

Aggregate

Net

migratio

n

aRank

Net

migratio

n

Rank

Net

migratio

n

Rank

In-m

igratio

nOut-m

igratio

nNet

migratio

n

Rank

Net

Rank

Guangdong

1799

196

111063

343

110281

270

113890

1613

12277

222

135420

249

1Zhejiang

273

30

241745

54

34021

106

28407

1339

7067

128

212560

88

2Shanghai

610

66

22005

62

22650

70

34934

401

4533

82

39798

69

3Beijin

g606

66

31715

53

41916

50

53851

406

3445

62

47682

54

4Jiangsu

319

35

5667

21

71963

52

44895

1894

3002

54

55951

42

5Fujian

104

11

10722

22

61132

30

62515

1114

1401

25

63359

24

6Tianjin

171

19

7388

12

8802

21

71499

213

1286

23

72647

19

7Xinjiang

437

48

4925

29

5395

10

8841

287

554

10

82311

16

8Liaoning

248

27

6375

12

9257

07

91181

685

495

09

91375

10

9Hainan

3804

1288

03

1133

01

10339

236

103

02

11262

02

10Nei

Mongol

159

17

81

160

418

23

01

14829

648

181

03

10201

01

11Ningxia

40

1541

01

137

00

11239

151

8902

12141

01

12Tibet

2703

1335

01

146

00

1292

6229

01

1485

01

13Qinghai

1702

144

60

116

12

00

13183

150

3301

138

00

14Yunnan

104

11

9335

10

101

320

315

632

1089

457

08

161

500

115

Shanxi

8709

1149

02

121

350

416

499

794

295

05

152

940

216

Shandong

90

116

2601

151

990

517

1341

2015

674

12

198

560

617

Jilin

134

15

222

750

919

315

08

18345

854

509

09

171

233

09

18Shaanxi

25

03

172

960

920

572

15

21735

1347

613

11

181

506

11

19Gansu

77

08

203

571

121

376

10

19260

1047

787

14

201

597

11

20Hebei

74

08

191

020

317

378

10

20925

2017

1092

20

211

646

12

21Chongqing

NA

NA

NA

655

20

231

010

27

23737

1844

1107

20

222

772

19

22Heilongjiang

188

20

236

392

022

825

22

22323

1463

1140

21

232

792

20

23Guizhou

107

12

219

703

024

1235

32

24592

2681

2088

38

244

400

31

24Guangxi

450

49

261

551

48

251

726

45

25600

2821

2221

40

255

948

42

25Hubei

44

05

181

604

50

262

214

58

27846

3804

2958

54

276

820

48

26Jiangxi

347

38

252

445

76

281

977

52

26699

3483

2784

50

267

553

53

27Hunan

532

58

282

899

90

302

827

74

28690

4592

3902

71

28 1

0160

71

28Henan

514

56

271

839

57

273

154

83

29432

5430

4999

91

311

0506

74

29Anhui

662

72

292

579

80

293

165

83

30824

5526

4702

85

301

1108

78

30Sichuan

1294b

141b

303

806

118

313

178

84

311055

4988

3933

71

291

2211

86

31To


n10661

32330

38042

55228

136261


ofthenatio


nbincludes

Chongqing

SourcesNPSSO

(199

7)SCamp

NBS(200

220

0720

12)

194 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12





196 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







198 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






200 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12



























202 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12


























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12

Table

1Major

aggregatemigratio

nandurbanpo

pulatio

nstatistics19


HukouMigrants

(Yearlyflow

figures)

Non

-HukouPop

ulation

(Stock

figures)

Defactopopulatio

nin

urbanareas

lsquoTem

porary

Populationrsquo

lsquoRural

Migrant

Laborrsquo

Rural-hukou

populatio

nin

urbanareas

Policestatistics

Registeredwith

MPS(m

id-year)


NBSestim

ates

basedon

samplesurveys

Authorrsquosestim

ates

NBSstatistics

Geographicboundary

(tocross)

Citytownor

township

Township-

levelunit

Countyor

city

Tow

nship

Not

applicable

Not

applicable

Minim

umlength

ofstay

(for

those

with

outlocalhukou)

Nominim

um3days

6months

6months(unless

otherw

isespecified)

Away

foroutsidework

(for

6monthsor

more)

6months

6months

Series

AB

CD

EF

G(

)H

1982

1730

66(1year)

465

217

2145

1987

1973

152

a260

c640

231

2767

1990

1924

216

(1year)

663

220

3020

1995

1846

497

291

b750

694

197

3517

2000

1908

448

1444

1210

1366

298

4591

2001

1701

551

1486

309

4806

2002

1722

598

1047

1528

304

5021

2003

1726

699

1139

1495

285

5238

2004

1949

780

1182

1514

279

5428

2005

1933

867

1531

1258

1531

272

5621

2006

2060

953

1321

1564

271

5771

2007

2084

1044

1370

1630

275

5938

2008

1892

1166

1404

1670

275

6067

2009

1677

1222

1453

1716

276

6219

2010

1701

1314

2614

2214

1534

2056

309

6656

2011

1554

1586

6908

Notes

andsources

MPS=Ministryof

Public

Security


Statistics

a the

geographic

boundary

isbasedon

citycounty

ortown

bthegeographic

boundary

isbasedon

county-level

units

c refersto

1988

AMPS(1988ndash

2010)NBSamp


2011)C

andDNBS(1988)SC

ampNBS(1985

1993200220072012)andNPSSO

(1997)E2002


NBS

compliedby

Cai

andChan(2009

Table1)2009


NBS(2010

2011


ates

inLuet

al(2002)

andthey

may

notbe

fully

comparableto

recent

NBSfiguresFEstim

ates

derivedfrom


apercentage


Chan(2012a)2011

figure

isfrom

Ma(2012)

190 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12












Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12








192 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12








Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12

Table2


nin

China19

90ndash201

0(inthou

sand

s)


1990

ndash1995

1995

ndash2000

2000

ndash2005

2005

ndash2010

1990

ndash2010

Aggregate

Net

migratio

n

aRank

Net

migratio

n

Rank

Net

migratio

n

Rank

In-m

igratio

nOut-m

igratio

nNet

migratio

n

Rank

Net

Rank

Guangdong

1799

196

111063

343

110281

270

113890

1613

12277

222

135420

249

1Zhejiang

273

30

241745

54

34021

106

28407

1339

7067

128

212560

88

2Shanghai

610

66

22005

62

22650

70

34934

401

4533

82

39798

69

3Beijin

g606

66

31715

53

41916

50

53851

406

3445

62

47682

54

4Jiangsu

319

35

5667

21

71963

52

44895

1894

3002

54

55951

42

5Fujian

104

11

10722

22

61132

30

62515

1114

1401

25

63359

24

6Tianjin

171

19

7388

12

8802

21

71499

213

1286

23

72647

19

7Xinjiang

437

48

4925

29

5395

10

8841

287

554

10

82311

16

8Liaoning

248

27

6375

12

9257

07

91181

685

495

09

91375

10

9Hainan

3804

1288

03

1133

01

10339

236

103

02

11262

02

10Nei

Mongol

159

17

81

160

418

23

01

14829

648

181

03

10201

01

11Ningxia

40

1541

01

137

00

11239

151

8902

12141

01

12Tibet

2703

1335

01

146

00

1292

6229

01

1485

01

13Qinghai

1702

144

60

116

12

00

13183

150

3301

138

00

14Yunnan

104

11

9335

10

101

320

315

632

1089

457

08

161

500

115

Shanxi

8709

1149

02

121

350

416

499

794

295

05

152

940

216

Shandong

90

116

2601

151

990

517

1341

2015

674

12

198

560

617

Jilin

134

15

222

750

919

315

08

18345

854

509

09

171

233

09

18Shaanxi

25

03

172

960

920

572

15

21735

1347

613

11

181

506

11

19Gansu

77

08

203

571

121

376

10

19260

1047

787

14

201

597

11

20Hebei

74

08

191

020

317

378

10

20925

2017

1092

20

211

646

12

21Chongqing

NA

NA

NA

655

20

231

010

27

23737

1844

1107

20

222

772

19

22Heilongjiang

188

20

236

392

022

825

22

22323

1463

1140

21

232

792

20

23Guizhou

107

12

219

703

024

1235

32

24592

2681

2088

38

244

400

31

24Guangxi

450

49

261

551

48

251

726

45

25600

2821

2221

40

255

948

42

25Hubei

44

05

181

604

50

262

214

58

27846

3804

2958

54

276

820

48

26Jiangxi

347

38

252

445

76

281

977

52

26699

3483

2784

50

267

553

53

27Hunan

532

58

282

899

90

302

827

74

28690

4592

3902

71

28 1

0160

71

28Henan

514

56

271

839

57

273

154

83

29432

5430

4999

91

311

0506

74

29Anhui

662

72

292

579

80

293

165

83

30824

5526

4702

85

301

1108

78

30Sichuan

1294b

141b

303

806

118

313

178

84

311055

4988

3933

71

291

2211

86

31To


n10661

32330

38042

55228

136261


ofthenatio


nbincludes

Chongqing

SourcesNPSSO

(199

7)SCamp

NBS(200

220

0720

12)

194 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12





196 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







198 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






200 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12



























202 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12


























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12












Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12








192 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12








Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12

Table2


nin

China19

90ndash201

0(inthou

sand

s)


1990

ndash1995

1995

ndash2000

2000

ndash2005

2005

ndash2010

1990

ndash2010

Aggregate

Net

migratio

n

aRank

Net

migratio

n

Rank

Net

migratio

n

Rank

In-m

igratio

nOut-m

igratio

nNet

migratio

n

Rank

Net

Rank

Guangdong

1799

196

111063

343

110281

270

113890

1613

12277

222

135420

249

1Zhejiang

273

30

241745

54

34021

106

28407

1339

7067

128

212560

88

2Shanghai

610

66

22005

62

22650

70

34934

401

4533

82

39798

69

3Beijin

g606

66

31715

53

41916

50

53851

406

3445

62

47682

54

4Jiangsu

319

35

5667

21

71963

52

44895

1894

3002

54

55951

42

5Fujian

104

11

10722

22

61132

30

62515

1114

1401

25

63359

24

6Tianjin

171

19

7388

12

8802

21

71499

213

1286

23

72647

19

7Xinjiang

437

48

4925

29

5395

10

8841

287

554

10

82311

16

8Liaoning

248

27

6375

12

9257

07

91181

685

495

09

91375

10

9Hainan

3804

1288

03

1133

01

10339

236

103

02

11262

02

10Nei

Mongol

159

17

81

160

418

23

01

14829

648

181

03

10201

01

11Ningxia

40

1541

01

137

00

11239

151

8902

12141

01

12Tibet

2703

1335

01

146

00

1292

6229

01

1485

01

13Qinghai

1702

144

60

116

12

00

13183

150

3301

138

00

14Yunnan

104

11

9335

10

101

320

315

632

1089

457

08

161

500

115

Shanxi

8709

1149

02

121

350

416

499

794

295

05

152

940

216

Shandong

90

116

2601

151

990

517

1341

2015

674

12

198

560

617

Jilin

134

15

222

750

919

315

08

18345

854

509

09

171

233

09

18Shaanxi

25

03

172

960

920

572

15

21735

1347

613

11

181

506

11

19Gansu

77

08

203

571

121

376

10

19260

1047

787

14

201

597

11

20Hebei

74

08

191

020

317

378

10

20925

2017

1092

20

211

646

12

21Chongqing

NA

NA

NA

655

20

231

010

27

23737

1844

1107

20

222

772

19

22Heilongjiang

188

20

236

392

022

825

22

22323

1463

1140

21

232

792

20

23Guizhou

107

12

219

703

024

1235

32

24592

2681

2088

38

244

400

31

24Guangxi

450

49

261

551

48

251

726

45

25600

2821

2221

40

255

948

42

25Hubei

44

05

181

604

50

262

214

58

27846

3804

2958

54

276

820

48

26Jiangxi

347

38

252

445

76

281

977

52

26699

3483

2784

50

267

553

53

27Hunan

532

58

282

899

90

302

827

74

28690

4592

3902

71

28 1

0160

71

28Henan

514

56

271

839

57

273

154

83

29432

5430

4999

91

311

0506

74

29Anhui

662

72

292

579

80

293

165

83

30824

5526

4702

85

301

1108

78

30Sichuan

1294b

141b

303

806

118

313

178

84

311055

4988

3933

71

291

2211

86

31To


n10661

32330

38042

55228

136261


ofthenatio


nbincludes

Chongqing

SourcesNPSSO

(199

7)SCamp

NBS(200

220

0720

12)

194 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12





196 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







198 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






200 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12



























202 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12


























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12








192 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12








Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12

Table2


nin

China19

90ndash201

0(inthou

sand

s)


1990

ndash1995

1995

ndash2000

2000

ndash2005

2005

ndash2010

1990

ndash2010

Aggregate

Net

migratio

n

aRank

Net

migratio

n

Rank

Net

migratio

n

Rank

In-m

igratio

nOut-m

igratio

nNet

migratio

n

Rank

Net

Rank

Guangdong

1799

196

111063

343

110281

270

113890

1613

12277

222

135420

249

1Zhejiang

273

30

241745

54

34021

106

28407

1339

7067

128

212560

88

2Shanghai

610

66

22005

62

22650

70

34934

401

4533

82

39798

69

3Beijin

g606

66

31715

53

41916

50

53851

406

3445

62

47682

54

4Jiangsu

319

35

5667

21

71963

52

44895

1894

3002

54

55951

42

5Fujian

104

11

10722

22

61132

30

62515

1114

1401

25

63359

24

6Tianjin

171

19

7388

12

8802

21

71499

213

1286

23

72647

19

7Xinjiang

437

48

4925

29

5395

10

8841

287

554

10

82311

16

8Liaoning

248

27

6375

12

9257

07

91181

685

495

09

91375

10

9Hainan

3804

1288

03

1133

01

10339

236

103

02

11262

02

10Nei

Mongol

159

17

81

160

418

23

01

14829

648

181

03

10201

01

11Ningxia

40

1541

01

137

00

11239

151

8902

12141

01

12Tibet

2703

1335

01

146

00

1292

6229

01

1485

01

13Qinghai

1702

144

60

116

12

00

13183

150

3301

138

00

14Yunnan

104

11

9335

10

101

320

315

632

1089

457

08

161

500

115

Shanxi

8709

1149

02

121

350

416

499

794

295

05

152

940

216

Shandong

90

116

2601

151

990

517

1341

2015

674

12

198

560

617

Jilin

134

15

222

750

919

315

08

18345

854

509

09

171

233

09

18Shaanxi

25

03

172

960

920

572

15

21735

1347

613

11

181

506

11

19Gansu

77

08

203

571

121

376

10

19260

1047

787

14

201

597

11

20Hebei

74

08

191

020

317

378

10

20925

2017

1092

20

211

646

12

21Chongqing

NA

NA

NA

655

20

231

010

27

23737

1844

1107

20

222

772

19

22Heilongjiang

188

20

236

392

022

825

22

22323

1463

1140

21

232

792

20

23Guizhou

107

12

219

703

024

1235

32

24592

2681

2088

38

244

400

31

24Guangxi

450

49

261

551

48

251

726

45

25600

2821

2221

40

255

948

42

25Hubei

44

05

181

604

50

262

214

58

27846

3804

2958

54

276

820

48

26Jiangxi

347

38

252

445

76

281

977

52

26699

3483

2784

50

267

553

53

27Hunan

532

58

282

899

90

302

827

74

28690

4592

3902

71

28 1

0160

71

28Henan

514

56

271

839

57

273

154

83

29432

5430

4999

91

311

0506

74

29Anhui

662

72

292

579

80

293

165

83

30824

5526

4702

85

301

1108

78

30Sichuan

1294b

141b

303

806

118

313

178

84

311055

4988

3933

71

291

2211

86

31To


n10661

32330

38042

55228

136261


ofthenatio


nbincludes

Chongqing

SourcesNPSSO

(199

7)SCamp

NBS(200

220

0720

12)

194 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12





196 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







198 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






200 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12



























202 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12


























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12








Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12

Table2


nin

China19

90ndash201

0(inthou

sand

s)


1990

ndash1995

1995

ndash2000

2000

ndash2005

2005

ndash2010

1990

ndash2010

Aggregate

Net

migratio

n

aRank

Net

migratio

n

Rank

Net

migratio

n

Rank

In-m

igratio

nOut-m

igratio

nNet

migratio

n

Rank

Net

Rank

Guangdong

1799

196

111063

343

110281

270

113890

1613

12277

222

135420

249

1Zhejiang

273

30

241745

54

34021

106

28407

1339

7067

128

212560

88

2Shanghai

610

66

22005

62

22650

70

34934

401

4533

82

39798

69

3Beijin

g606

66

31715

53

41916

50

53851

406

3445

62

47682

54

4Jiangsu

319

35

5667

21

71963

52

44895

1894

3002

54

55951

42

5Fujian

104

11

10722

22

61132

30

62515

1114

1401

25

63359

24

6Tianjin

171

19

7388

12

8802

21

71499

213

1286

23

72647

19

7Xinjiang

437

48

4925

29

5395

10

8841

287

554

10

82311

16

8Liaoning

248

27

6375

12

9257

07

91181

685

495

09

91375

10

9Hainan

3804

1288

03

1133

01

10339

236

103

02

11262

02

10Nei

Mongol

159

17

81

160

418

23

01

14829

648

181

03

10201

01

11Ningxia

40

1541

01

137

00

11239

151

8902

12141

01

12Tibet

2703

1335

01

146

00

1292

6229

01

1485

01

13Qinghai

1702

144

60

116

12

00

13183

150

3301

138

00

14Yunnan

104

11

9335

10

101

320

315

632

1089

457

08

161

500

115

Shanxi

8709

1149

02

121

350

416

499

794

295

05

152

940

216

Shandong

90

116

2601

151

990

517

1341

2015

674

12

198

560

617

Jilin

134

15

222

750

919

315

08

18345

854

509

09

171

233

09

18Shaanxi

25

03

172

960

920

572

15

21735

1347

613

11

181

506

11

19Gansu

77

08

203

571

121

376

10

19260

1047

787

14

201

597

11

20Hebei

74

08

191

020

317

378

10

20925

2017

1092

20

211

646

12

21Chongqing

NA

NA

NA

655

20

231

010

27

23737

1844

1107

20

222

772

19

22Heilongjiang

188

20

236

392

022

825

22

22323

1463

1140

21

232

792

20

23Guizhou

107

12

219

703

024

1235

32

24592

2681

2088

38

244

400

31

24Guangxi

450

49

261

551

48

251

726

45

25600

2821

2221

40

255

948

42

25Hubei

44

05

181

604

50

262

214

58

27846

3804

2958

54

276

820

48

26Jiangxi

347

38

252

445

76

281

977

52

26699

3483

2784

50

267

553

53

27Hunan

532

58

282

899

90

302

827

74

28690

4592

3902

71

28 1

0160

71

28Henan

514

56

271

839

57

273

154

83

29432

5430

4999

91

311

0506

74

29Anhui

662

72

292

579

80

293

165

83

30824

5526

4702

85

301

1108

78

30Sichuan

1294b

141b

303

806

118

313

178

84

311055

4988

3933

71

291

2211

86

31To


n10661

32330

38042

55228

136261


ofthenatio


nbincludes

Chongqing

SourcesNPSSO

(199

7)SCamp

NBS(200

220

0720

12)

194 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12





196 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







198 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






200 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12



























202 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12


























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12

Table2


nin

China19

90ndash201

0(inthou

sand

s)


1990

ndash1995

1995

ndash2000

2000

ndash2005

2005

ndash2010

1990

ndash2010

Aggregate

Net

migratio

n

aRank

Net

migratio

n

Rank

Net

migratio

n

Rank

In-m

igratio

nOut-m

igratio

nNet

migratio

n

Rank

Net

Rank

Guangdong

1799

196

111063

343

110281

270

113890

1613

12277

222

135420

249

1Zhejiang

273

30

241745

54

34021

106

28407

1339

7067

128

212560

88

2Shanghai

610

66

22005

62

22650

70

34934

401

4533

82

39798

69

3Beijin

g606

66

31715

53

41916

50

53851

406

3445

62

47682

54

4Jiangsu

319

35

5667

21

71963

52

44895

1894

3002

54

55951

42

5Fujian

104

11

10722

22

61132

30

62515

1114

1401

25

63359

24

6Tianjin

171

19

7388

12

8802

21

71499

213

1286

23

72647

19

7Xinjiang

437

48

4925

29

5395

10

8841

287

554

10

82311

16

8Liaoning

248

27

6375

12

9257

07

91181

685

495

09

91375

10

9Hainan

3804

1288

03

1133

01

10339

236

103

02

11262

02

10Nei

Mongol

159

17

81

160

418

23

01

14829

648

181

03

10201

01

11Ningxia

40

1541

01

137

00

11239

151

8902

12141

01

12Tibet

2703

1335

01

146

00

1292

6229

01

1485

01

13Qinghai

1702

144

60

116

12

00

13183

150

3301

138

00

14Yunnan

104

11

9335

10

101

320

315

632

1089

457

08

161

500

115

Shanxi

8709

1149

02

121

350

416

499

794

295

05

152

940

216

Shandong

90

116

2601

151

990

517

1341

2015

674

12

198

560

617

Jilin

134

15

222

750

919

315

08

18345

854

509

09

171

233

09

18Shaanxi

25

03

172

960

920

572

15

21735

1347

613

11

181

506

11

19Gansu

77

08

203

571

121

376

10

19260

1047

787

14

201

597

11

20Hebei

74

08

191

020

317

378

10

20925

2017

1092

20

211

646

12

21Chongqing

NA

NA

NA

655

20

231

010

27

23737

1844

1107

20

222

772

19

22Heilongjiang

188

20

236

392

022

825

22

22323

1463

1140

21

232

792

20

23Guizhou

107

12

219

703

024

1235

32

24592

2681

2088

38

244

400

31

24Guangxi

450

49

261

551

48

251

726

45

25600

2821

2221

40

255

948

42

25Hubei

44

05

181

604

50

262

214

58

27846

3804

2958

54

276

820

48

26Jiangxi

347

38

252

445

76

281

977

52

26699

3483

2784

50

267

553

53

27Hunan

532

58

282

899

90

302

827

74

28690

4592

3902

71

28 1

0160

71

28Henan

514

56

271

839

57

273

154

83

29432

5430

4999

91

311

0506

74

29Anhui

662

72

292

579

80

293

165

83

30824

5526

4702

85

301

1108

78

30Sichuan

1294b

141b

303

806

118

313

178

84

311055

4988

3933

71

291

2211

86

31To


n10661

32330

38042

55228

136261


ofthenatio


nbincludes

Chongqing

SourcesNPSSO

(199

7)SCamp

NBS(200

220

0720

12)

194 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12





196 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







198 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






200 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12



























202 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12


























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12





196 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







198 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






200 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12



























202 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12


























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12





196 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







198 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






200 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12



























202 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12


























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







198 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






200 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12



























202 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12


























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12







198 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






200 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12



























202 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12


























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12









Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






200 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12



























202 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12


























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12






200 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12



























202 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12


























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12



























202 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12


























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12



























202 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12


























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12


























Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12
























204 KW Chan

Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12










Dow

nloa

ded

by [

Kam

Win

g C

han]

at 1

217

30

Dec

embe

r 20

12

Chan Migration China MD

Documents

Transcript of Chan Migration China MD