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INTERNALMIGRATIONINTHECOUNTRIESOFASIA:LEVELS,AGES,ANDSPATIALIMPACTSADRI-WP-2017/001

ElinCharles-Edwards1,2,MartinBell1,2,AudeBernard1,2andYuZhu1,3

1AsianDemographicResearchInstitute,ShanghaiUniversity,China

2UniversityofQueensland,Brisbane,Australia

3CenterforPopulationandDevelopmentResearch,FujianNormalUniversity,China

CorrespondingAuthor:ElinCharles-Edwards,e.charles-edwards@uq.edu.au

About

ADRIWorkingPaperSeries

TheAsianDemographicResearchInstitute(ADRI)atShanghaiUniversityaimstoplayaleading role in Asia for comprehensively investigating the population dynamics andaddressingitssocioeconomicandenvironmental implicationsinacomparativemanner. Intheearly2016,itstartedserveastheheadquarteroftheAsianMetaCenterforPopulationand Sustainable Development Analysis which provides a platform for Asian regionalcollaboration of research and training. The International Post-Graduate Program forPopulation and Sustainable Development at Master and PhD levels offers courses andtrainingopportunities inbothEnglishandChineseforChineseand internationalstudents.Research at ADRI is fundamental and lays a scienti�c basis for the formulation ofappropriatepopulationandsustainabledevelopmentpolicies.TheacademicworkofADRIis disseminated in the form of book, journal articles, teaching texts, research reports,monographs,andworkingpapers.TheADRIWorkingPaperseriesincludesacademicworksbyADRI facultymembers,post-graduate students, adjunct andvisiting fellows. It aims toprovide a forum for work in progress which seeks to elicit comments and generatediscussions.ADRIWorkingPapersareavailableinelectronicformatatwww.adri.edu.cn 99ShangdaRd.,BaoshanDistricit Shanghai,China

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INTERNALMIGRATIONINTHECOUNTRIESOFASIA:LEVELS,AGES,ANDSPATIALIMPACTSABSTRACT

The countries of Asia have undergone an epoch of rapid demographic change. While

considerable effort has been exerted in the study of fertility and mortality, studies of

internalmigrationarecomparativelyfew,despiteitsmajorroleinredistributingpopulations

within countries. This paper reports on a comparative study of internalmigration for 30

countries inAsia,drawingona commonquantitative frameworkdeveloped in the IMAGE

project (www.imageproject.com.au). Three aspects of internal migration are explored:

intensity, age profile, and spatial impact, drawing on both recent and lifetime data to

explore currentpatternsandhistorical trends.Comparisons reveal that internalmigration

intensities, while on average lower than in other parts of the world, are highly variable

acrosscountries.This isconnectedtokey indicatorsofdevelopmentbutalsoto individual

countries’progressionthroughtheurbantransition.MigrationintensitiesinAsiapeakatan

earlierageandaremoreconcentratedthaninotherpartsoftheworld.Analysisofspatial

impactshighlights thecontributionofmigration tourbanisation throughoutAsia,butalso

theenduringimpactsofconflict,forceddisplacements,andgovernmentpoliciesonnational

migrationsystems.

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1.Introduction

Asia is the largestandmostpopulousof thesevencontinents,hometomore than three-

fifths of theworld’s population. Stretching across almost half the globe, the continent is

home to diverse populations, cultures, political systems and economies, reflecting the

combinedforcesofgeographyandhistory.Theprogressofdemographictransition inAsia

has been rapid. Across the continent as a whole, fertility declined from 5.8 children per

womenin1950-1955to2.2in2010-2015(UnitedNations,2015a).Lifeexpectancyincreased

from42.1yearsin1950-1955to71.6yearsin2010-2015(UnitedNations,2015a).Thisshift

hasbeenaccompaniedbyrapidurbanisationofthepopulationfrom17.5percenturbanin

1950toalmost50percenturbanin2015(UnitedNations,2015b),andmassinternational

migration,withanestimated78millionAsianslivingoutsidetheircountryofbirthin2010

(Bell and Charles-Edwards, 2013). Considerable effort has been exerted in study of the

patterns andprocessesof fertility,mortality and internationalmigration, bothwithin and

acrossthecountriesofAsia.Bycontrast,studiesofinternalmigrationarecomparativelyfew.

Thisisdespitethefactthat3.5timesasmanyAsiansareinternalmigrants(280million)as

internationalmigrants(78million)(BellandCharles-Edwards,2013).

ThispaperseekstoaddressthedeficitofcomparativestudiesofinternalmigrationinAsia,

usingaquantitativeframeworkthatdrawsonthedataandmethodsdevelopedunderthe

IMAGE project (Comparing Internal Migration Around the GlobE –

https://imageproject.com.au). The study seeks to go beyond a simple description of

contemporary patterns to provide a rigorous comparison using robust quantitative

measures that capture key aspects of internalmigration. It also seeks to provide insights

into theway internalmigration has evolved over time. The paper begins in Section 2 by

summarisingrelevantpriorwork,identifyingkeythemesintheliterature,andtheextentof

geographic coverage. In Section 3, we provide a brief overview of the IMAGE project. A

major constraint to comparative research on internal migration is variation between

countriesinthetypeofdatathatarecollected,thetemporalintervaloverwhichmigration

is measured, and the spatial resolution at which migration is captured. We review the

internalmigrationdataavailableforAsiancountriesinSection4anddiscusstheirstrengths

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andlimitations.Sections5through7presenttheresultsofouranalysis,focusingonthree

discrete, but inter-related, dimensions of migration: the overall intensity or level of

movementinthecountry(Section5),themigrationageprofile(Section6),andtheimpact

ofmigrationonsettlementsystems(Section7).Eachofthesedimensionsprovidesaunique

perspectiveonthenatureandimplicationsofthemigrationprocessandtogethertheyoffer

a complementarypictureof themigration system. In Section8,we compare results from

the analysis of recent and lifetime migration data to explore the evolution of internal

migrationinthecountriesofAsia,andsetoutanagendaforfutureresearch.

2.InternalMigrationinAsia:AnOverview

Internal migration in the countries of Asia remains underexplored with respect to key

aspects ofmobility, and studies are limited in their geographic coverage. Cross-national

studies of internal migration are especially rare, reflecting a lack of data availability and

issues of data comparability. Amrith (2011) provided a comprehensive history tracing the

evolutionofmigration inAsiasincethemid-19thCentury.Drawingonhistoricrecordsand

published case studies, this descriptive work challenged the notion of a traditionally

sedentarysociety,and identified longstanding linkagesbetween internaland international

migration in the countries of Asia: a theme also highlighted in contributions by Skeldon

(2006) and Hugo (2016). More recently, Fielding (2015) has described contemporary

migration systems in the countries of North-East, East and South-East Asia, revealing

significantregionalvariationsinthepatternsofinternalmigrationandtheirchangingdrivers

over time. In North-East Asia, high levels of internal migration coincided with rapid

urbanisationandindustrialisationinthedecadesfollowingtheSecondWorldWar(andthe

KoreanWar).Laterdecadesweremarkedbyamigrationturnaround,characterisedbynet

flowsfromurbantoregionalcentres,followingshiftsinthelocationofmanufacturing.The

urbantransitioncommencedlaterinEastAsia,withrural-urbanflowsremainingadominant

featureofcontemporarymigrationsystems. InChina, rural-urban flowsaredominatedby

large scale migration of the “floating population” from western, inland provinces to the

eastern, coastalprovinces (Zhu,2007), andpartlyoffsetby frontierwardmigration. In the

countriesofSouth-EastAsia, internalmigrationhasbeendominatedbytwocountervailing

processes: the migration of rural populations to Asian megacities and frontierward

migration to remote regions for the purposes of primary production, often facilitated by

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largescalegovernmentprograms.TheworksofAmrith (2011),Fielding (2015)andothers

(see also Deshingkar, 2006) underscore the links between economic development and

migration but also point to the impact of government intervention on Asian migration

systems. These studies provide extremely valuable insights into migration processes for

selected countries, but include limited quantification of internal migration intensity and

impact.Theyarealsorestrictedingeographiccoverage,concentratingontheeasternhalfof

thecontinent.

While comparative studies remain rare, a large and diverse body of country-specific

researchhasdevelopedsincethemiddleofthe20thCentury. Thesubjectandgeographic

locus of research has shifted over time. Early scholarship was preoccupied with rural to

urban migration as countries underwent rapid urbanisation, and was focused on the

countries of East and South Asia. The 1980s saw a broadening of interest to include the

selectivityofmigration, for example the feminisationof internalmigration flows in South

East Asia (see e.g. Phongpaichit, 1992, Thadani and Todaro, 1984), echoing trends in

international migration research. Links to development became an important subject of

inquiryinthe1990s,coterminouswiththeAsianeconomicmiracle,andthiscontinuestobe

animportanttopic(DeWindetal.,2012,Chan,2012,Mendola,2012).Thefirstreferencesto

environment-drivenmigrationemergedinthe1990s(Subedi,1997)andhavecontinuedto

grow,particularlyinregionsvulnerabletotheeffectsofclimatechange,forexampleinthe

countries of South Asia including Bangladesh (Hassani-Mahmooei and Parris,2012, Hugo,

2011).Thefirstdecadesofthenewmillenniumhaveseenasignificantexpansioninboththe

volume and diversity of research. Three broad clusters of contemporary research can be

identified: studies of migration impacts at origins and destinations; studies of different

migration forms; and studies of spatial patterns. Studies of impacts at origins and

destinations are the largest group. These dealwith subjects ranging from the impacts on

familyleftbehind(seee.g.Abasetal.,2009,Changetal.,2011,Adhikarietal.,2011)tothe

impactsonthelabourmarketatthedestination(seee.g.ElBadaouietal.,2014,Mengand

Zhang, 2010, Phongpaichit, 1993). The collection of papers edited by Zhu et al. (2013)

provideaconcisesynthesis.Asecondclustercomprisesstudiesofmigrantcharacteristics.

Forexample,migrantselectionandhealthhasemergedasanimportanttopicofinquiryin

China(seee.g.Chen,2011,Chatterjee,2006,Xiang,2003,Mouetal.,2011),whilemarriage

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migration isprominent in the literature,especially in thecountriesofSouthandEastAsia

(NedoluzhkoandAgadjanian,2010,Davin,2007,Fan,2008,Fulford,2015).Afinalclusterof

researchrelatestothespatialpatternsofinternalmigration.Interestinthistopichasgrown

inrecentyears, inpartreflecting increaseddataavailability.Studiesofmigrationpatterns,

including both rural to urban and interprovincial migration, have been undertaken in a

number of countries including China (Chan, 2013, Gu, 2014, Liu and Shen, 2014, Poncet,

2006,), Vietnam (Phan and Coxhead, 2010), Indonesia (van Lottum and Marks, 2012),

Malaysia (Mohd Razani Mohd, 2009), Myanmar (Department of Population 2014) and

Kyrgyzstan (Alymbaeva, 2013). There has also been a renewal of interest in the role of

temporary and circularmobility,most notably in reference to China’s floating population

(ZhuandChen,2010,Zhu,2007).

Pastresearchprovidessomeinsightsintotheroleofmigrationwithrespecttolivelihoods,

economic development and the growth of cities and regions. Despite this, our overall

understandingofthepatternsandprocessofinternalmigrationinAsiaremainsdescriptive,

fragmentedandlimitedingeographicscope.Thetiming,patternsanddriversofmigration

clearlyvaryacrossthecountriesofAsia,butthereremainsalargegapinourunderstanding,

particularly in Central and West Asia. The assembled evidence from the IMAGE project

suggeststhatinternalmigrationintensitiesinAsiaarelowerthaninotherpartsoftheworld,

although there are pockets of high mobility (Charles-Edwards et al., 2016), and that

migrantsareyounger than inother continents (Bernardetal., 2014b). In somecountries,

too, the impact ofmigration on urbanisation (i.e. through urban-ruralmigration)may be

lessthaninotherpartsoftheworlddueto“insitu”urbanisation(Zhu,2000,Jones,1997),

although this is yet to be systematically tested across multiple countries. Temporary

migrationalsoappearstobeanimportantcomplementto,andinsomecountriesperhapsa

substitutefor,permanentmigration.ThediversityofhumanmobilityinAsia,andthevariety

ofresearch,isthereforeextensive,butasyetsomewhatdisparateandfragmentary.

Buildingon theworkof the IMAGEproject,whichprovided the first global synthesis, the

currentstudyseekstoprovideanintegratedaccountofinternalmigrationinthecountries

ofAsiausingacommonanalyticframeworkandstandardmetrics.Italsoseekstoaddanew

dimensiontotheglobalIMAGEprojectbyextendingtheanalysistoincorporateatemporal

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perspectivethroughtheuseoflifetimemigrationdata,collectedmorewidelyinAsiathanin

mostotherworldregions.

3.Acomparativeframework:IMAGE-Asia

TheIMAGEprojectwasamultiyear, internationalcollaborativeprogramofresearchwhich

sought to provide wide-ranging, cross-national comparisons of internal migration for

countriesaroundtheglobe.Theprojectcoalescedinfourdiscretemodules:

1. A global inventory identifying the types of internal migration data collected by

nationalstatisticalofficesaroundtheworld(Belletal.,2015a)

2. Arepositorycontainingkeysetsofinternalmigrationdataforselectedcountriesina

standardisedformat,togetherwithdigitalboundaries(Belletal.,2014);

3. Specialised analytical software developed to compute a suite of robust migration

indicators(IMAGEStudio)(Stillwelletal.,2014);

4. Aseriesofpapersdetailinganalyticalmethodsandcomparingcountriesonvarious

aspectsofpopulationmobility.(Bernardetal.,2017,Charles-Edwardsetal.,2016).

TheIMAGEprojectfocusedonfourdiscretedimensionsofmigrationthatwereconsidered

keytounderstandingthemultifacetednatureofpopulationmovement(Belletal2002).The

firstwasmigrationintensity,thatistheoveralllevelorrateofmovementwithinacountry

(Bell et al., 2015b). The second dimension was age: one of the key characteristics that

shapes the propensity to move. Migration is a highly selective process, peaking in early

adulthood, and declining at older ages, but with evidence ofmarked variations between

countries. The third dimension identified was migration impact. Migration is singularly

significant among demographic processes in its ability to rapidly redistribute populations.

This redistributivepotential is captured inmeasuresofmigration impact and is especially

pertinent to the process of urbanisation. Migration distance was the final dimension

examined, reflecting the underlying spatiality of themigration process, and the frictional

effectofdistance inconstrainingpopulationmovement.Resultsoftheanalysiscomparing

countriesgloballyoneachofthesedimensionswerereportedinaseriesofpapers(Stillwell

etal.,2016,Reesetal.,2016,Bernardetal.,2014a,Belletal.,2015b).

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Drawing on that body of work, the current paper seeks to provide a detailed synthesis

aimedatadvancingunderstandingofinternalmigrationinthecountriesofAsia,paralleling

asimilarregionalstudyofLatinAmericaandtheCaribbean(Bernardetal.,2017).Herewe

focus on three dimensions ofmobility: intensity, age and impact, setting asidemigration

distance, but adding a temporal perspective which exploits the lifetime migration data

available inmany Asia countries. As well as extending earlier work that considered both

internal and international movements, the paper aims to provide the framework for a

coordinated series of country-specific studies of migration being conducted under the

auspicesoftheAsianDemographicResearchInstitute,IMAGE-Asia.

4.InternalMigrationDataintheCountriesofAsia

Whocollectswhat?

Thelackofcomparabledatahasbeenakeyimpedimenttocross-nationalstudiesofinternal

migration (Bell et al., 2015a). Issues arise with respect to: 1) differences in the types of

internal migration data collected (e.g. events, transitions, lifetime or last move); 2) the

intervaloverwhichmigration ismeasured(e.g.oneor fiveyears);and3) thespatialunits

intowhichcountriesaredivided,givingrise to theModifiableArealUnitProblem(MAUP)

(Belletal.,2015a).Whilenotstrictlyequivalent,differenttypesofmigrationdata(1)canbe

comparedgivenashortenoughtemporalinterval(e.g.forexamplethecomparisonofone

year event and one year transition data), aswell as judicious use of data on duration of

residencetofilterdataonpreviousmove(e.g.tocomparefiveyeartransitionwithfiveyear

durationdata(Belletal.,2015b).Thecomparisonofmigrationdatameasuredfordifferent

intervals(2)islesstractableduetothedifferentialimpactofreturnandrepeatmigrationon

one and five year migration measures (Long and Boertlein, 1990). Issues of spatial

comparabilityand theMAUP (3)areanotherbarrier to cross-national studies (Openshaw,

1977). The IMAGE Project sought to address this aspect of comparability through the

developmentof scale freemetrics fordifferentdimensionsof internalmigration including

intensity(Belletal.,2015b),age(Bernardetal.,2014b)andimpact(Reesetal.,2016),and

alsomadeconsiderableprogressinidentifyingtheeffectoftheMAUPonmigrationmetrics.

Thecalculationofthesemetricsrequiresdataatahigh levelofspatial resolutionwhich is

regrettably lacking formanycountries inAsia.Nevertheless, it is clear thatunderstanding

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thetype, intervalandspatialresolutionofmigrationdataisakeyfirststepinundertaking

anycrossnationalcomparisons.

Ofthe47UNMemberStatesinAsia,allbutfour1havecollecteddataoninternalmigration

in the past two decades (Table 1). The Census is the most common source of internal

migration data in Asia, with 41 countries collecting data in the 2000 or 2010UN Census

Round. Sixteencountriescollected internalmigrationdata inaPopulationRegister,while

26countrieshavecollecteddataviaanationally representativepopulationsurveysuchas

USAID’s Demographic and Health Survey (DHS).While Censuses are widely implemented

acrossAsia,PopulationRegistersareconcentratedinthecountriesofEastandCentralAsia.

Population Surveys tend to be more important in the less developed countries of Asia,

includingpartsofSouth,CentralandWesternAsia,wherestatisticalsystemsarenotyetso

welldeveloped.

Table1Internalmigrationdatacollection,CountriesofAsia,from1995

RegionofAsia

Census

Register

Survey(D

HS)

Anyda

taheldin

IMAG

ERe

pository

Totalcou

ntrie

sin

region

1year

5YR

Lifetim

e

Lastm

ove

Central 0 0 4 3 5 5 3 5East 0 5 2 1 4 1 5 5South-East 2 4 7 4 2 7 7 11South 1 4 8 6 0 5 7 9Western 4 2 10 8 5 8 8 17

Total 7 15 31 22 16 26 30 47Notes:AcountryspecificlistingisprovidedinAppendixA.

Different collection instruments capture different types of migration data. Population Registers

measuremigrationevents(i.e.thenumberofmoves),whereasCensusesandSurveysusuallycollect

informationonmigrationtransitions,(i.e.thenumberofmigrants)measuredeitheroveradiscrete

period(generallyoneyear,fiveyears,orsincebirth),orwithrespecttothelastmove.Table1shows

thefrequencywithwhichdifferentdatatypesarecollectedacrossthefiveregionsofAsia.Detailsfor

individualcountriesareprovidedinAppendixA.

1 Brunei Darussalam; Kuwait; United Arab Emirates; Lebanon

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Lifetimedata,whichcompareindividuals’placeofcurrentresidencewiththeirplaceofbirth,arethe

mostcommon,collectedby31of47countriesinAsia.Nextaredataonplaceofpreviousresidence,

regardlessofthetimingofmove(referredtoinTable1as‘lastmove’data),whicharecollectedby

28countries.In22countriesthesedatacanbecombinedwithinformationondurationofresidence

togenerateameasureofmigrationoveradefinedinterval.Thesedataaremostcommonlycollected

inSouth,CentralandWesternAsia.FixedintervaldataarelesscommoninAsiathaninotherparts

oftheworld.Fiveyeardata,whichcomparerespondents’currentplaceofresidencewiththeirplace

of residence five yearsearlier are collected inarounda thirdof countries (15/47),whileoneyear

transitiondataarecollectedinjustsevencountriesspanningWesternandSouthEastAsia.

A number of national migration surveys are conducted in Asia including the Malaysian National

MigrationSurvey,theIndianNationalSocialSurveyandthePakistanLabourForceSurvey.Themost

ubiquitousisUSAID’sDemographicandHealthSurvey(DHS)whichcollectscomparableinformation

oninternalmigrationforanumberofcountriesacrossAsia.UptoandincludingWave5oftheDHS,

astandardquestiononplaceofpreviousresidenceanddurationofcurrentresidencewasincluded

inallsurveys.DHSgenerallyonlycapturethemobilityofwomenaged15-49andlackspatialdetail

beyondabroad rural/urbanclassification.Thesedatadohowever fill gaps ingeographiccoverage

whereCensusandRegisterdataarenotavailable,particularlyinCentralandWesternAsia.

Whatdataareavailable?

Differences in data collectionpractice are complicatedby a lack of data availability,with detailed

migration statistics rarely included in standard statistical releases. Furthermore, unlike births and

deaths, data on internal migration are not produced with a view to generating internationally

comparable statistics. Internal migration statistics are conspicuous by their absence from central

repositories hosted by theUnitedNations and other international organisations. In response, the

IMAGEprojectassembledarepositoryofinternalmigrationdatafor135of193UNmemberstates,

including for 30 countries of Asia. Data were drawn from national statistical offices, custom

tabulations from the IPUMS-International database (Minnesota Population Centre, 2017) and

USAID’s DHS Survey (ICF International. 2012) There is wide variation in the types of data these

countriescollect,andinthelevelofdetailavailable(Belletal.,2015a).Figure1indicatestheoverall

geographiccoverageoftheinternalmigrationdataintheIMAGErepositoryavailableforthisstudy.

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Figure1Summaryofdataholdings,IMAGE-Asia

With respect to recent migration, one year transition data are only available for four of the 47

countries:Cyprus, Israel,JapanandTurkey. Fiveyeartransitiondataprovidewidercoverage,with

dataavailable for10countries.Thesecanbecoupledwithdataon lastmove filteredby fiveyear

duration of residence to deliver data on migration over a five year interval for a further seven

countries.Togetherthese17countriesencompassmorethan80percentofthepopulationofAsia2.

CoverageismostcompleteinEastandSouthAsiabutdecreasesmovingwestward.Significantgaps

indataholdingsexistinCentralandWesternAsia.

The IMAGERepositoryholds lifetimemigrationdata for19of the31Asiancountrieswhichcollect

this type of data. The major gap is in Central Asia but lifetime data are also missing from the

Repository for anumberof countries in EastAsia (Republic of Korea, Japan) andWestAsia,most

notablytheGulfStates.Althoughlifetimedataarethemostubiquitous,theyaregenerallyregarded

as lessuseful forcomparativepurposesbecausetheymeasurethecumulativemigrationhistoryof

national populations andmaskmore recent trends. This is particularly problematic formeasuring

migrationintensityandfortheanalysisofmigrationagepatternsbecausecountriesdifferwidelyin

agecomposition.Ontheotherhandlifetimedatacanprovidevaluableinsightsintothelongerterm

effectsofmigrationbyshowingtheextenttowhichpopulationshaveleft,orbeendisplacedfrom,

theirplacesofbirth:ausefulcomplementtotheoneorfiveyeardatawhichmeasurepatternsover

arecentinterval.

2 In comparison, one year transition and/or event data are only available for five Asian countries.

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Differences in migration data type and interval are not the only barriers to cross-national

comparisons. Because different migration metrics call for different data inputs, data must be

availableinaconsistentandappropriateformat.Table2showsthenumberofcountriesforwhich

appropriate data are available in the IMAGE Repository for the measurement of the three

dimensions ofmigration examined in this paper:Migration Intensity; Age at PeakMigration, and

MigrationImpact.

Table2Summaryofdataavailabilityforanalysisofdiscretedimensionsofinternalmigration

RegionIntensity Age Impact

Anymeasure To

tal

coun

tries

Recent(5year)ACMIs

Lifetime DHS(5YR) Recent

(5year+1year)

Lifetime Rural-Urban

Central 1 2 3 0 1 0 1 3 5East 5 2 0 1 4 2 0 5 5South 3 4 3 3 3 4 1 7 11South-East 6 6 4 6 6 6 6 8 9West 2 5 4 3 2 5 5 7 17

Total 17 19 14 13 16 17 13 30 47Note: thediscrepancybetweenthecounts forrecent intensityand impactmeasures isduetodifferences in

dataformat.DHSdataareidentifiedseparatelyduetoitsincompletepopulationcoverage,limitedtowomen

aged25-49.SeeAppendixBforfulltableofcountries.

As indicated, the data enable some aspect ofmigration intensity to be calculated for all

regions of Asia, although coverage decreases steadily moving westward. While data are

availableforthewholeofEastAsia,estimatesofmigrationintensitiesarepossibleforfewer

thanhalfofthecountriesinSouth,CentralandWestAsia.Geographiccoveragefordataon

ageatmigrationismorelimited,withinformationforonly13countries,principallyinSouth

East,SouthandWestAsia.Broadergeographiccoverage isavailable formigration impact,

reflectingthewideavailabilityoflifetimemigrationdata,butisalittlemorelimitedthanfor

migrationintensityasitscomputationrequiresdetailedflowmatrices,showingmovements

betweenoriginsanddestinations.Thecentralchallengeforthisstudyistheidentificationof

regionalpatternsandprocesses fromthispatchworkofdata. In the followingsectionswe

examineeachofthesedimensionsinturn,firstoutliningtheindicatorsweuse,andshowing

howthesemeasuresarecomputed,thenpresentingtheresults.

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5.MigrationIntensity:Howmuchmovement?

The level ofmigration apparentwithin a country, ormigration intensity, depends on the

typeofmigrationdatacollected(i.e.events;transitions;lastmove);theintervaloverwhich

migration is measured (i.e. one year; five year; lifetime) and the spatial framework.

Migration intensity is particularly sensitive to the number of spatial units used in the

analysis,risingsteadilyasthenumberofspatialunitsincreases,liftingthepotentialforany

givenresidentialrelocationtocrossazonalboundary(Belletal.,2015d).Followingtheearly

work of Long (1991), Bell et al. (2002) argue that the only reliable basis onwhich cross-

national comparisons can be made is to utilise a measure that captures all permanent

changesofresidentialaddresswithinacountry,irrespectiveofthedistancemoved(seealso

Rees et al., 2000). This is measured by the Aggregate CrudeMigration Intensity (ACMI),

computedas:

ACMI=M/P*100

whereMisthetotalnumberofinternalmigrants(transition/lastmovedata)or

migrations(eventdata)inagiventimeperiodandisexpressedasapercentageofP,

thenationalpopulationatriskofmoving.

Unfortunately,veryfewcountriescollectinformationonallchangesofaddress.Onlyfourof

the30countriesexaminedinthispapercollectinformationonallchangesofresidence.To

address this problem, we adopt the approach developed by Courgeau, Bell andMuhidin

(2012)toestimatetheACMIbyfittingaregressionequationtoCrudeMigrationIntensities3

generatedforeachcountryatdifferinglevelsofspatialscaleusingtheIMAGEStudio.The

method is elaborated in Bell et al. (2015b)who adopted the same approach to generate

comparable estimates for a global sample of 92 countries covering 80% of the global

population. This raises the number of Asian countries for which we have estimates of

recentmigrationfrom4to17,comprisingathirdofallcountriesinAsia.

The results (Table 3) reveal considerable variability in the level ofmobility. ACMIs range

froma lowof 5.2per cent in India to 52.8per cent in SouthKorea. This upper value for

3 CMI = M/P*100 for any number of regions

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SouthKoreaisanoutlierinthesample,sittingmorethantwostandarddeviationsabovethe

samplemean. It is importanttonotethattheACMIforSouthKorea isbasedonobserved

data rather, and cannot therefore be explained away as some anomaly arising from the

estimationprocess.TheaveragefiveyearACMIforcountriesinthesampleis17.9%(15.5%

withSouthKoreaexcluded).Thisislowerthantheglobalmeanof21.0%calculatedforthe

full61countries forwhichtherequisitedataareheld inthe IMAGErepository(Belletal.,

2015b), and suggests that, at least over the most recent period, Asian populations are

relativelysedentarywhencomparedwithotherpartsoftheworld.Given,thehighlevelsof

emigrationfrommanyAsiancountries,itispossiblethatsomesubstitutionbyinternational

movements isoccurring. Incontrast tootherpartsof theworld,suchasEuropeandLatin

America,thereisnoclearregionalisationinthespatialpatternofmigrationintensities(Bell

etal.,2015b),althoughcountriesinSouthEastAsiaappeartorecordintensitiesbelowthe

average. Other regions display a mix of low intensities in some countries alongside high

intensitieselsewhere.

Table3FiveyearACMIforselectedcountriesofAsia

Year Type ACMI Method HDI(2015)India 2001 5DR 5.2 ESTIMATED 0.62NorthKorea 2008 5Y 6.3 ESTIMATED naNepal 2001 5Y 8.3 ESTIMATED 0.56Iraq 1997 5DR 8.5 ESTIMATED 0.65Philippines 2000 5Y 9.3 ESTIMATED 0.68Iran 2011 5DR 11.0 ESTIMATED 0.77Thailand 2000 5DR 11.2 ESTIMATED 0.74Indonesia 2010 5Y 12.4 ESTIMATED 0.69Vietnam 2009 5Y 12.6 ESTIMATED 0.68China 2000 5Y 12.8 ESTIMATED 0.74Malaysia 2000 5Y 16.4 OBSERVED 0.79Cambodia 1998 5DR 18.4 ESTIMATED 0.56Kyrgyzstan 1999 5DR 22.4 ESTIMATED 0.66Mongolia 2000 5Y 27.4 ESTIMATED 0.73Japan 2000 5Y 27.8 OBSERVED 0.90Israel 1995 5Y 28.2 OBSERVED 0.90SouthKorea 2000 5Y 52.8 OBSERVED 0.90AsianMean 17.9 GlobalMean 21.0

Globalmeanacrosssampleof61countriesheldintheIMAGERepository(Source:Belletal.,2015d)

Thelevelofhumandevelopmentmayprovidepartoftheexplanationforthesedifferences,

withSouthKorea,IsraelandJapanrecordinghighACMIsandcorrespondinglyhighlevelsof

humandevelopment,ascapturedbytheHumanDevelopmentIndex(HDI),whilecountries

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suchasIndiaandNepalrecordbothlowACMIsandlowHDIscores.Asimpleregressionof

2015 HDIs against ACMI yields an R2 of 0.46. Countries such as Mongolia record higher

ACMIs thanmight be expected given the level of humandevelopment. ThehighACMI in

Mongoliamightreflectacultureofmobility,similartowhathasbeenobservedforthenew

world countries of the USA, Australia and Canada (Long, 1991) but equally might be

attributedtoadramaticshiftinlivelihoodsawayfromtranshumanceandnomadismwhich

manifestasruraltourbanmigration(Fielding,2015).

DHSdataprovideapotentialmechanismtowidenthegeographiccoveragetoencompass

morecountriesinWestandCentralAsia.DHSdataarenotdirectlycomparabletothefive

yearACMIsshowninTable3fortworeasons.First,theACMIisameasureofallchangesof

address,whiletheDHSsimplycaptureslengthofresidenceinalocalityorplace.Secondly,

the5yearACMImeasuresmobilityfortheentirepopulationaged5andover,whereasthe

DHSisconfinedtowomenaged15to49.Table4showsintensitiesbasedonDHSdatafor

14countries.ThelowestmigrationintensityisrecordedinArmenia(6.9%)andthehighest

in the Philippines (27.6%). The average CMI for the sample is 16.5%. As with the ACMI

estimates,noclearregionalisationofmigrationintensitiesisevident.Moreconcerningisthe

lackofagreementbetweenACMIsandDHSdataforthefourcountrieswhichappearinboth

samples.Nepal and thePhilippines, lowmobility countries according to the censusbased

ACMIs,recordthehighestDHSCMIs.Incontrast,CambodiarecordsalowCMIbasedonDHS

data, but a relatively high ACMI from census data. It is not possible to reconcile these

differences but the variation almost certainly arises from issues of population coverage,

particularlywith respect to sexdifferentials, but also the vaguewordingof themigration

questionintheDHSwhichsimplyasksrespondentsfortheirdurationofresidenceintheir

current “place” of residence. While DHS data have previously been used by several

commentatorstoexploreinternalmigration,theseresultscastconsiderabledoubtsontheir

validityasameasureofoverallmigrationintensity.

17

Table4FiveyearCMIs(DHS)andACMIs(Censusdata)forselectedcountriesofAsia

Year CMI(DHS) ACMIArmenia 2000 6.9

Cambodia 2000 8.5 18.4Timor-Leste 2009-10 9.1

Uzbekistan 1996 10.4VietNam 2002 10.6Kazakhstan 1999 15.4Azerbaijan 2006 15.9Jordan 2002 18.1Turkey 2003 19.5SriLanka 2006-7 20.1

Kyrgyzstan 1997 24.0 22.4Bangladesh 1999-2000 24.2

Nepal 2001 24.7 8.3Philippines 2003 27.6 9.3Samplemean 16.5

Lifetimedataprovideanalternativelensthroughwhichtoassesscross-nationaldifferences

in migration intensity. Data are available for 19 countries, but comparisons are difficult

because countries vary widely with respect to the size and number of units over which

lifetimemigrationismeasured.Moreover,placeofbirthisgenerallycollectedatarelatively

coarse geographic scale (eg states or provinces, rather than municipalities or counties),

whichprecludesuseof the IMAGEStudiotogenerate lifetimeestimateofACMIs.Despite

this, lifetimemigration intensities do provide intriguing insights into the extent to which

individualswithinapopulationhavemadeasignificantshiftawayfromtheirregionofbirth.

AsshowninTable5theproportionswhohaverelocatedrangefromalowof4.1percent

between the 35 States of India to a high of 32.7 per cent between the 20 provinces of

Bhutan.Countrieswhichappeartohaverelativelyhigh lifetimeintensities includeBhutan,

Kazakhstan,Malaysia,MongoliaandTurkey.Countrieswithrelativelylowintensitiesinclude

Timor-Leste and Jordan. For the former group, these figures provide stark evidence of

significantredistributionsofpopulationwithinthenationalterritory,implyingmarkedshifts

inthepatternofhumansettlement.Fortheremainingcountries,theresultssuggestgreater

stability in the settlement pattern, at least at this spatial scale.We examine patterns of

redistribution within countries in greater detail below, but it is notable that comparing

lifetimemigrationintensitiesagainstthecurrent(fiveyear)migrationintensitiesatthesame

spatialscaledeliversapositivecorrelation(R2=0.69)acrossthe12countriesforwhichboth

typesofdataareavailable.

18

Table5LifetimeCMIsforselectedcountriesofAsia

Year No.ofregions LifetimeIntensityIndia 2001 35 4.1China 2000 31 6.2Iraq 1997 15 8.3Indonesia 2000 26 8.4Jordan 2004 11 9.0SaudiArabia 2004 13 9.2TimorLeste 2004 13 11.9Cambodia 2008 24 13.6Armenia 2001 11 13.7Nepal 2001 74 14.1Myanmar 2014 74 14.6SriLanka 2012 25 16.8Thailand 2000 76 17.0KyrgyzRepublic 2000 52 19.2Mongolia 2000 21 20.2Malaysia 2000 15 20.7Turkey 1990 61 23.5Kazakhstan 2009 13 26.4Bhutan 2005 20 32.7

How do we reconcile these differences between the two measures? Migration theory

suggeststhatthe levelof internalmigrationwithinacountryrisesthenfallswithprogress

throughthemobilitytransition(Zelinsky,1971).Timeseriesdataoninternalmigrationare

rare,andusuallycovera limitedtimespan,butasimpleratioof lifetime intensityagainst

recentmigrationintensityprovidessomeusefulinsightintotemporaltrendsinaformthat

isdirectlycomparableacrosscountries(Figure2).

Figure2RatioofLifetimetoFiveYearMigrationIntensities

19

Theresultssuggest that fiveAsiancountries -China,Mongolia,KyrgyzRepublic,Myanmar

andCambodia-arecurrentlyundergoingahistoricallyintenseperiodofinternalmigration

(i.e.currentintensitiesarehighcomparedwithhistoricalexperience,resultinginlowratios).

Highmigrationintensitiesarecharacteristicsofthemiddlestagesofthemobilitytransition

(and hence development), driven by large scale movements from rural to urban areas.

Somewhat lower ratiosof around four areobserved in six countries: Indonesia,Malaysia,

Thailand, India,Nepal and Iraq.Moderate ratios are expectedboth in early stages of the

mobilitytransition,whereinternalmigrationintensitiesareincreasingbutareyettoreach

peaklevels,andatlaterstages,whenmobilityisonceagainmoderatingasspaceeconomies

and settlements systems again stabilise. The highest ratio is found in Armenia, where

lifetime migration intensity is more than six times greater than the current value.

ContemporarymigrationintensityinArmeniaisthereforeatarelativelylowlevelwhenset

againsthistoricalnorms.Sucharesultisnotunexpectedforcountriesatalatestageinthe

mobilitytransition,wheresignificanthistoricalshiftshavetakenplace,ortherehavebeen

majordisruptionsinthemigrationsystem.InArmenia,forexample,highlifetimemigration

might be tied to relocations following dissolution of the USSR. Historically lowmigration

may also be a product of rapid population ageing, as more people move into older age

brackets,wheremobilityiscomparativelylow.

6.AgeatMigration

Migration is an age-selective process, with young adults being the most mobile group.

Irrespectiveofaggregatelevelsofmobility,thepropensitytomovetypicallypeaksatyoung

adult ages, then steadily declineswith increasing age, sometimes rising again around the

age of retirement. This broad age profile is replicated, with some variations, at various

spatialscalesandinavarietyofcountries(CastroandRogers,1983),includingJapan,Korea

andThailand(KawabeandLiaw,1992,Ishikawa,1978).Despitethesepersistentregularities,

thereisincreasingevidenceofsystematicvariationsintheagesatwhichmigrationoccurs,

particularlyatyoungadultages.

Figures 3a and 3b report, for a few selected countries, age-specific migration intensities

normalisedtounitysothatmigrationageprofilesareindependentfromvariationsinoverall

20

intensities. It is important to bear in mind that migration is measured over a five-year

interval. Since age is recorded at the end of the observation period, migrants will have

movedonaverage2.5yearsearlierthantheagewhichisrecorded,assumingthatmigration

is evenly distributed over the five-year interval. Figure 3a reveals marked variations

betweenthreeAsiancountrieswithmigrationreachingitspeakbeforetheageof22inIndia

comparedto23inArmeniaand24.5inthePhilippines.Broadvariationsarealsoapparent

withrespecttothedegreeofconcentrationofmigrationactivityatyoungadultages,witha

strongerconcentrationofmigration inyoungadulthoodin Indiathan inArmeniaor inthe

Philippines.

Thesedifferencesreflectsimilarvariationsinmigrationagepatternsbetweenworldregions,

thoughataworld scale thedifferencesareevenmorepronounced.Figure3bshows that

migration within China peaks at age 21 and is strongly concentrated around the peak,

whereasmigrationinBrazilandPortugalisdispersedacrossabroaderagerangeandpeaks

later in adulthood, at 25 and 29 years, respectively. This result closely conforms to a

previouslyidentifiedpatternofastrongconcentrationofmigrationintheearly20sinChina

and South-East Asia that stands in contrast with late and dispersed migration peaks in

EuropeandNorthAmerica(Bernardetal.,2014a,BellandMuhidin,2009).

3a.AcrossAsia 3b.AcrosstheWorld

Figure3Age-specificMigrationIntensities,selectedcountriesSource:Authors’ calculations basedon five-year-intervalmigration data reportedby single-year age groups.Migrationdatawerenormalisedtosumtounityandsmoothedusingkernelregression(BernardandBell,2015)

Tosystematicallyestablishtheextentofvariationintheageprofileofmigrationforalarger

sampleofAsiancountries,itispossibletousetwoindicatorsthatsummarisemigrationage

21

patterns: the age at which migration peaks, and the intensity of migration at the peak.

These two indicators capture two thirds of the inter-country variance in migration age

profiles (Bernardet al., 2014b) and,unlike the conventionalRogersparameters, have the

significant benefit of being intrinsically meaningful. The age at which migration peaks

captures how early in lifemigration occurs,while the intensity ofmigration at the peaks

gaugesthedegreeofconcentrationofmigrationactivityatyoungadultages.

Figure4plots theageatwhichmigrationpeaksagainstnormalisedmigration intensity at

the peak. To interpret the results against countries in other regions, the data have been

normalisedacrossaglobalsampleof33countriesfromallworldregionssothatthemeanis

zero and the standard deviation from the global mean is equal to one. With this

normalisation,aunitonthegraphrepresentsonestandarddeviationfromtheglobalmean,

whichrevealshowAsiancountriescomparetotherestoftheworld.Figure4showsthatin

allcountriesexceptthePhilippines,TurkeyandIran,migrationpeaksatanageyoungerthan

theglobalmean.ThisdifferenceisparticularlypronouncedinVietnamandIndonesiawhere

theageatthepeakliesmorethanonestandarddeviationfromtheglobalmean,withpeaks

around21yearsofage.Figure4alsoshowsastrongconcentrationofmigrationatyoung

adult ages, with all countries except Iraq, the Philippines, Turkey and Iran displaying

intensitiesatthepeakabovetheglobalmean.Infact,nocountryoutsideAsiafallsintothe

upper left quadrant that corresponds to early and concentrated migration activity. This

confirms the distinctive age structure of migration in most Asian countries, best

characterisedas‘earlyandconcentrated’.AcloserinspectionofFigure4,however,reveals

important variationswithin Asia. The intensity at the peak is just one standard deviation

from the global mean in China and Armenia, whereas it falls more than two standard

deviations from the mean in Vietnam and India, indicating a very high concentration of

migrationactivityatyoungadultages.

22

Figure4AgeatmigrationpeakagainstnormalisedmigrationintensityatpeakSource:IMAGERepository

Note:Measureswerederivedfrommigrationdatadisaggregatedbysingleyearsofage,normalisedtounity,andsmoothedusingKernelregression(BernardandBell,2015).Theglobalmeanwasestimatedforasampleof33countriesencompassingallworldregions.Gridlinesarelocated1standarddeviationfromtheglobalmean.

Across theworld, theagepatternofmigrationhasbeen shown to closelymirror theage

structure of key life-course transitions, in particular the completion of education, labour

marketentry,unionformationandfamilyformation(Bernardetal.,2014a).InmanyAsian

societies, theprocessofbecominganadult is guidedby social structures andnorms that

supportearlyandrapidtransitions intoadultstatuses (YeungandAlipio,2013).Thus, it is

theconcentrationoflife-coursetransitionsinearlyadultlifethatunderpinsthepronounced

concentrationofmigrationactivityintheearlytwenties,asshowninFigure4.

Acrosstheworld,womentendtoprogresstoadultrolesearlierthanmen(Lloyd,2005)and

it is the gendered pattern of transitioning to adulthood that underpins the younger

migrationageprofilethatiscommonlyfoundamongwomen.Inallregions,includingAsia,

migrationpeaksonaverage2.5yearsearlierforwomenthanformen(Bernardetal.,2014a).

Of course, not allmoves are triggeredby life-course transitions, as young adultsmove in

EARLY LATE

CONCEN

TRATEDDISPERSED

23

responses toawiderangeofopportunitiesandconstraints.Thus,contextual factorsmay,

on occasion, triggermigration directly, as in the case of changes in economic conditions

(Molloy et al., 2014) or in the level of social and political openness. This is one possible

explanationforIraq’sdispersedmigrationpatterns.

7.MigrationImpact

Academic and policy interest in internalmigration is driven in large part by its ability to

transform national population distributions, particularly its contribution to urbanisation.

Indeed, the urban transition is one of the great dynamics of our time and has been

particularly pronounced in Asia. Despite the significance of internal migration to

urbanisation globally, its actual contribution to population redistribution, has proven

difficult to measure. Most commonly, net migration is computed simply as the residual

componentofpopulationchange,oncenatural increasehasbeen taken intoaccount,but

thisconfoundsinternalandinternationalmigration,andinheritsalltheenumerationerrors

intheothercomponentsofchange.Dataonruraltourbanmigrationprovideamoredirect

measureof theway internalmigrationcontributes tourbanisation,but suchdataarenot

widely collected, and cross-national comparisons are plagued by inconsistencies in the

definition of urban and rural regions. The available data for Asian countries have been

assembled in Figure5,which shows thebalanceof flowsbetween rural andurbanareas.

Countries are ordered by the share of their population living in urban areas in 2015.

Migrationwithincountriesatearlystagesoftheurbantransition(Cambodia,Timor-Leste)is

predominately between rural areas. The share of rural-urban migration increases as the

levelofurbanisation rises (seeVietnam,Thailand, Indonesia,Kyrgyzstan),beforedeclining

again as urban to urbanmigration becomes the dominantmigration form (e.g.Malaysia,

Israel).

24

Figure5ShareofRural-rural;rural-urban;urban-rural,urban-urban,recentmigrationflows,Asia

Note:Thepercentageofthepopulationlivinginurbanareasin2015andtheMERRUareshowninbrackets.Countriesarerankedbythepercentageofthepopulationlivinginurbanareain2015.

TheRuraltoUrbanMigrationEffectivenessRatio(MERRUshowninparenthesesinFigure5)

providesasimplesummarymetriccapturingthebalancebetweenruraltourbanflowsand

counterflows:

MERRU=100×(MRU-MUR)/(MRU+MUR)

whereMRUaremigrationflowsfromruraltourbanareasandMURaremigration

flowsfromurbantorural.

Values for the MERRU vary between -100 and +100 with positive values signifying a net

balanceinfavourofurbanareas,andthemagnitudeoftheindicatorshowingthestrength

oftheredistributionforthegivenvolumeofmovement.FromFigure5,theMERURishighest

for countries at relatively early stages of the urban transition (e.g. Timor Leste and India

where the population is less than 30 per cent urban). Developed countries with highly

urbanisedpopulations(e.g.IsraelandMalaysia)havenegativevaluesofMERUR,suggesting

25

counter-urbanisation processes traditionally associated with late stages of the urban

transition.Both the shareofoverall flowswhichare rural tourban flows,and theMERRU,

reveal that the direction of flows, and therefore the redistributive impact of migration,

changeascountriesurbanise.

Even where data on rural to urban migration are available, they provide only a crude

measure of population redistribution, based on a coarse dichotomy between rural and

urbanareas.Amorerobustapproachtotheanalysisofmigrationimpactwasimplemented

by Rees and Kupiszewski (1999) in their study of European countries, and subsequently

refinedbyReesetal(2016)usingpopulationdensityasaproxyforurbanisation.Reesetal.

(2016)proposedatheoreticalrelationshipbetweenmigrationandthepopulationdensityof

regions,asdepictedinFigure6.Theindividualgraphsembeddedinthelargergraphplotthe

netmigrationrateagainstthelogofpopulationdensityforallregionsofacountry,withthe

solid line indicating the hypothesised relationship across regions, captured empirically via

linear regression. Populationdensity is effectively adopted as a surrogate for the level of

urbanisationwithinindividualregions.Positiveslopesindicatethatmoredenselypopulated

regionsaregainingthroughnet internalmigration,while lessdenselypopulatedareasare

losing.Thesteepertheslope,thegreatertherateofredistribution.Thelogisticcurveonthe

larger graph traces the shift from low to high levels of urbanisation (the y axis) as

development proceeds through a series of phases (the x axis). The conceptual model

indicates that migration from low to high density regions, proceeds in a progressive

sequence indicated by the changing steepness of the slope, accelerating as development

takesoff(Stages1to2),reachingapeakastherateofdevelopmentpeaks(Stage2),then

slowing at later stages of development (Stage 3) when countries become predominantly

urban.InStage4andbeyond,migrationflowsbecomemorecloselybalancedwithnetflows

potentiallyoscillatingbetweengainsorlossesinmoreurbanareas,thelattercorresponding

totheclassicprocessofcounter-urbanisation.

26

Figure6AtheoreticalframeworklinkingdevelopmenttopopulationredistributionthroughnetmigrationSource:AfterReesetal(2016)

In practice, of course, the relationship between population density and the rate of net

migration isnot as clear cut as themodelwould suggest.Nevertheless,Reeset al (2016)

foundempiricalsupportforthemodelacrossaglobalsampleof67countries.Hereweseek

tofurthertestthetheorisedrelationshipbetweeninternalmigrationimpactandtheurban

transitionfor22Asiancountriesforwhichsuitabledata(recentand/orlifetime)areavailable.

Linear regressionshavebeenestimatedusingdata for regionsweightedbypopulation to

reducetheinfluenceofregionswithsmallpopulationsontheoveralllinefit.Theresultsare

shown in Figure 7, the left hand panel depicting countries with relatively steep slopes,

indicatinghighlevelsofmigrationfromlowdensitytohighdensityregions,therighthand

paneldepictingcountrieswheretheslope,andthelevelofmigration,ismoremoderate.

27

Figure7FittedslopescapturingtherelationshipbetweenNMRandpopulationdensityfor17countriesbasedonrecentmigrationdata

Fourdistinctclustersofcountriescanbeidentified,twofromeachpanel.Afirstclusterwith

steep slopes is comprisedofMongolia andKyrgyzstan. Steeppositive slopes suggest that

the largestnetmigrationgainsareoccurring inthemostdenselypopulatedregions,while

thelargestnetmigrationlossesareoccurringinthemostsparselysettledregions.Asecond

groupof countrieswithmoremoderate, but still stronglypositive, slopes is comprisedof

Nepal, Vietnam, Thailand, China and Cambodia. Here, net movements from low to high

density regions continue, but at a more moderate pace. The third and fourth clusters

encompasstheremainingtencountries,allwithrelativelyflatslopes,thatarecharacteristic

oflatestagesoftheurbantransition.Thisgroupissub-dividedsimplybythehorizontalaxis,

withjustsevencountriesdisplayingmodestpositiveslopes(Malaysia,India,Japan,Turkey,

Japan and Iraq) while in three, (Iran, Indonesia and South Korea), the modest negative

slopesindicatethatthedirectionofmigrationhasreversed,suchthatnetgainsnowfavour

lessdenselysettledareas.

Tocontextualisetherelationshipbetweenmigrationandurbanisationweplottheserecent

migration-density slopes against the percentage of the population living in urban areas

(Figure7).TheresultsprovidesolidsupportfortherelationshiphypothesisedbyReesetal

(2016). The steepest positive slopes are recorded among countries midway through the

28

urban transition (G1). Moderate slopes are recorded in countries at early stages of the

transition (G2), while countries at late stages of the urban transition record moderate

positive or negative slopes (G3). India, Myanmar and Indonesia departing from the

theorised relationship, all recording slopes lower than anticipated by their level of

urbanisation. In India, the moderate slope is likely an outcome of having a very large

populationdistributedacross relatively fewspatial zones (35states).Rural tourban flows

occurringwithin IndianStatesaresimplynotcapturedby the relativelycoarsegeographic

frameworkonwhichmigrationdataareavailable.Asecondcontributingfactormaybethe

high levels of reciprocity in rural-urbanmigration flows in India. Rural-urbanmigration in

India ishighlymasculinised,wherebyyoungmenmigratetourbanareasattoaccumulate

wealth before returninghome (Tumbe, 2016). The circularity of rural-urban flows lessens

theoveralleffectivenessofmigration,asyoungcohortsarrivinginurbanareasareoffsetby

older cohorts returning to rural homes. By contrast, the modest slope recorded for

Indonesia likely reflects the diversity of internalmigration in that country. Large rural to

urban flows sit alongside customary modes of circulation, migration to frontier regions

(bothindependentandstatesponsored)andsignificantpopulationsof internallydisplaced

persons(Fielding,2015). InMyanmar,theweakpositiveslopesreflects large lossesfrom

denselypopulated States suchasAyeyarwady alongside largenet gains tootherdensely

populatedstatessuchasYangon.

Figure8Recentslopeagainst%populationinUrbanArea(variousyears)

Notes:%urbanreferstothedateclosesttotheCensusdata.Thisis2000inallcountriesexceptIran(2005)

andVietnam(2010)

29

To explore the link betweenurbanisation and internalmigrationover a longer time scale

and for more countries we fitted population-weighted regressions linking lifetime net

migrationratesagainstthelogofregionalpopulationdensities.

Lifetime data capture the cumulativemigration history of a country, andwhile they bias

recentmigrationmovements,particularly inveryyoungpopulations,theyalsoreflectpast

movementpatternsaggregatedovermanydecades.Figure9showslifetimeslopesplotted

for 14 countries. Clusters are not as distinct as for the recent migration data described

above,butfournaturalgroupingareevident.LargepositiveslopesareobservedforBhutan,

Turkey, East Timor and Mongolia, implying that over the past half century or so, the

dominantpatternof redistributionhasbeen fromrural tourbanareas.Thenextgroupof

countries(Cambodia,Nepal,Armenia,ThailandandMalaysia)havemoremoderatepositive

slopes.Whiletheoveralldirectionofflowshasbeenfromruraltourbanareas,thesemay

have been offset by recent processes of counter-urbanisation (e.g. Malaysia) and

frontierwardmigration(e.g.Thailand).OnlymodestpositiveslopesarefoundforIndia,Iraq

and China, probably because of the coarse geographic units for which lifetime data are

available,maskingrural-urbanflowswithinregionswhichmakeasignificantcontributionto

urbanisation(ProvincesinChinaandStatesinIndia).IndonesiaandSaudiArabiacomprisea

final group recording negative slopes. For Saudi Arabia this is a product of flows to

settlementsadjacenttooil fieldsat theexpenseofothermoredenselypopulatedregions

with non-resource related economic bases (Al Bassam, 2011). In Indonesia, the negative

slope is likely a product of longstanding population shifts away from Central Java to

Indonesia’s outer provinces supported in part by the government’s Transmigrasi program

(Fielding2015).

30

Figure9FittedslopescapturingtherelationshipbetweenNMRandpopulationdensityfor17countriesbasedonlifetimemigrationdata

Tocontextualiserecentmigrationimpactsagainstlongertermmigrationprocessesweplot

recentslopesagainstlifetimeslopesfor8countriesforwhichbothlifetimeandrecentdata

areavailable(Figure10).Theregressionlinescapturingrecentmigrationimpactareclosely

correlatedwiththelifetimeslopes(R2=0.70),suggestingthatthereisconsiderableinertiain

the internalmigrationsystem,but thereareclearlyanomalies. InChinaandMongolia the

redistributive impactof recent rural tourbanmigration ishigher thanwouldbeexpected

givenhistoricalflows,suggestingthatbothcountriesarecurrentlyundergoinganepochof

rapid redistribution. In Cambodia andMalaysia, on the other hand, the slope for recent

migration is lower than expected, suggesting that rural to urbanmigration is increasingly

offset by flows to less densely population regions, either through counterubanisation, or

movementtoresourcefrontiers,assuggestedabove.

31

Figure10Fittedlifetimeslopesplottedagainstrecentslopes,selectedcountries

Thisbriefexplorationofmigration-densityslopesindicatesthat internalmigrationinmany

countries of Asia has been driven by a much more complex set of forces than the

straightforwardpathway through theurban transition suggestedby theRees et al (2016)

model.As so cogentlydemonstratedby Fielding (2015), rural tourban flowsare justone

element of more complex migratory systems in the countries of Asia which include

frontierwardmigrationassociatedwiththeexploitationofprimaryresources(e.g.VietNam,

Philippines),migrationarising fromconflict (e.g.Myanmar, Laos)andgovernmentpolicies

on internal migration (e.g. Malaysia, Indonesia). In such a complex setting, normative

models based on a simple rural to urban transition will not suffice. Understanding the

historical and cultural setting in individual countries is critical to teasingout the interplay

between different forms of internal population movement which have shaped past and

contemporarymigrationpatterns.

8.Conclusion

32

Inadiverseregionspanning195°of longitudeand77°of latitude it ishardlysurprisingto

findconsiderablediversityinthemigrationexperienceofindividualcountries.Indeed,prior

work has brought to the fore the marked variability that exists between countries even

withinthesamegeographicregionofthecontinent(seee.g.Fielding,2015,Amrith,2011).

This paper has endeavoured to set aside these differences in the search for more

fundamentalunderlyingsimilarities,andtodosousingastandardanalyticalframeworkand

common statisticalmeasures. This goal has inevitably faced impediments posed bywide-

ranging differences in the way migration data are collected, the timeframes and spatial

frameworks used and the availability of data. Despite these impediments, a number of

consistentpatterns in themobility experienceof countries in theAsian regionhavebeen

identified.First,itisapparentthatmigrationintensityislowerthantheglobalaverage,but

thatthereisalsowidespreadvariabilityconnectedinparttokeyindicatorsofdevelopment.

Secondlythere isaconsistentpatternwithrespecttoage,withpeakmigration intensities

highly concentrated and early. The spatial patterning of recent migration flows provides

strong supportive evidence that internalmigration is performing a key role in the urban

transition across the Asian region, with most countries displaying movements consistent

withtheirprogressthroughtheurbantransition.Thesefindingsarebroadlyconsistentwith

thosefromtheglobalsampleofcountriesanalysedelsewhereusingsimilartechniques(Bell

etal.,2015b,Reesetal.,2016,Stillwelletal.,2016)andwithresultsforindividualregions

suchasLatinAmerica(Bernardetal.,2017).Whatisparticularlynovelfromthecurrentset

ofresultsisthediversityofspatialimpactsapparentfromthelifetimemigrationdatawhich

appearto inherentapanoplyofmigrationstreamsdrivenbyotherforces includingforced

resettlement,conflictandprimaryresourceexploitation.These inturnreflectthecultural,

economicandpoliticalhistoriesofindividualcountriesintheAsianregionandunderlinethe

needformorenuancedinvestigationofatacountryspecificlevel.

33

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AppendixA:Internalmigrationdatacollectionandavailability,2000and2010CensusRound

Census Register Survey

1YR

5YR

Lifetim

e

Duratio

n+

PPR

DHSor

Other

Survey

IMAG

Eda

ta

holdings

CentralA

sia Kazakhstan     ü ü ü ü ü

Kyrgyzstan  

ü ü ü ü ü Tajikistan  

ü   ü ü  

Turkmenistan  

ü ü ü ü  

Uzbekistan         ü ü ü

EastAsia

China   ü ü   ü   ü Japan   ü

  ü   ü

DPRofKorea   ü

      ü RepublicofKorea   ü

  ü ü ü

Mongolia   ü ü ü ü   ü

SouthEastAsia

BruneiDarussalam               Cambodia  

ü ü   ü ü

Indonesia   ü ü     ü ü LaoPDR ü

ü        

Malaysia ü ü ü     ü ü Myanmar  

ü ü     ü

Philippines   ü

    ü ü Singapore  

  ü    

Thailand  

ü ü   ü ü Timor-Leste  

ü ü   ü ü

Vietnam   ü     ü ü ü

SouthAsia

Afghanistan     ü ü   ü   Bangladesh   ü ü     ü ü Bhutan   ü ü ü     ü India  

ü ü   ü ü

Iran  

ü     ü Maldives ü ü ü         Nepal   ü ü     ü ü Pakistan  

ü ü   ü

SriLanka     ü ü     ü  ü

WesternAsia

Armenia     ü ü ü ü ü Azerbaijan ü

ü ü ü ü ü

Bahrain  

  ü     Cyprus ü

ü     ü ü

Georgia  

ü ü       Iraq  

ü ü   ü ü

Israel ü ü

ü ü   ü Jordan  

ü ü   ü ü

Kuwait  

        Lebanon  

     

Oman  

ü       Qatar  

ü        

SaudiArabia  

ü       ü Syria  

ü   ü  

Turkey ü ü ü   ü ü ü UnitedArabEmirates  

       

Yemen     ü     ü  

Dataheld 3 10 19 8 2 14 30

Collected(ü) 7 15 31 22 16 26 42

40

AppendixB:SummaryofResults

Region Country

CensusYear Intensity

Age

Impact(Slope) Rural- #SpatialUnits

Recent(ACMI)

Lifetime(Variable#units)

DHS5 Recent Lifetime UrbanMER Recent Lifetime

Central Kazakhstan 2009/1999(DHS) 22.5 12.9 16

Central Kyrgyzstan 1999/1997(DHS) 22.4 19.2 19.9 7.5 46.4 52 52

Central Tajikistan Central Turkmenistan Central Uzbekistan 1996(DHS) 8.6 East China 2000 12.8 6.2 21.5 2.6 0.2 31 31East Japan 2000 27.8 0.4 47East DPRofKorea 2008 6.3 10East RepublicofKorea 2006 52.8 -0.7 242East Mongolia 2000 27.4 20.2 8.5 37.2 21 21SouthEast BruneiDarussalam

SouthEast Cambodia 2008/2000(DHS) 18.4 13.6 6.7 23.3 1.9 4.3 40.9 149 24

SouthEast Indonesia 2000 12.4 11.0 21.0 -0.4 -0.3 30.8 494 26SouthEast LaoPDR SouthEast Malaysia 2000 16.4 20.7 22.5 0.6 4.3 -58.4 136 15SouthEast Myanmar 2014 14.6 0.0 0.1 15 15

SouthEast Philippines 2000/2003(DHS) 9.3 24.0 24.5 1620

SouthEast Singapore SouthEast Thailand 2000 11.2 17.0 23.0 3.0 4.4 41.0 76 76

SouthEast Timor-Leste 2004/2009-10(DHS) 8.9 7.8 3.9 62.1 13

SouthEast Vietnam 2009/2002(DHS) 12.6 8.9 20.8 3.6 58.0 63

South Afghanistan South Bangladesh 1999-00(DHS) 20.6 South Bhutan 2005 32.7 10.3 20South India 2001 5.2 4.1 21.8 0.4 0.2 50.0 35 35South Iran 2006 11.0 27.5 -0.3 -7.5 South Maldives

South Nepal 2001/2001(DHS) 8.3 14.1 20.6 21.5 4.4 2.4 63 74

South Pakistan

South SriLanka 2012/2006-7(DHS) 19.9 20.1 0.0 25

West Armenia 2001/2000(DHS) 13.7 5.6 23.3 2.4 -19.0 11

West Azerbaijan 2006(DHS) 13.2 West Bahrain West Cyprus West Georgia West Iraq 1997 8.5 8.3 22.3 0.0 1.1 -16.5 15 15West Israel 1995 28.2 -7.5 Counts

West Jordan 2004/2002(DHS) 9.0 15.2 -0.1 11

West Kuwait West Lebanon West Oman West Qatar West SaudiArabia 2004 9.2 -0.3 13West Syria West Turkey 1990 27.0 16.4 25.8 0.3 9.5 -6.9 61

41

West UnitedArabEmirates West Yemen TOTAL 17 19 14 13 16 17 13

42