asians on the move

8/18/2019 Asians on the Move

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Asians on the Move:Spouses, Dependantsand Households

Special Collection of Papers byChotib

Siew-Ean KhooSalut Muhidin

Zhou Hao

S.K. Singh

ASIAN M ETA C ENTRE RESEARCH PAPER SERIES

no. 8

ASIAN META CENTRE FOR POPULATION A ND SUSTAINABLE DEVELOPMENT A NALYSIS

HEADQUARTERS AT ASIA RESEARCH INSTITUTE NATIONAL UNIVERSITY of SINGAPORE


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Asians on the Move:Spouses, Dependantsand Households

Chotib currently works in the Demographic Institute, Faculty ofEconomics at the University of Indonesia as a Researcher. Healso teaches in the Masters Program for the Study of Populationand Human Resources Economics and the Masters Program forGeography, at the same university. He graduated from theDepartment of Geography, Faculty of Mathematics and NaturalSciences in 1991, and received his masters degree from theProgram for the Study of Population and Human ResourcesEconomics in 1998 from the University of Indonesia. Hisresearch interests are in migration and urbanization, urban andregional planning, population projection, multiregionaldemography, labour, population and sustainable development.

Siew-Ean Khoo is Executive Director of the Australian Centrefor Population Research School of Social Sciences, the Australian

National University (ANU). Prior to taking her current position,she was Senior Lecturer in the Demography Programme at theANU. Her research and publications have focused on familyformation issues, ethnic demography and immigrant settlement,she has a Doctor of Science in Population Sciences from HarvardUniversity.

Salahudin (Salut) Muhidin is a researcher from theDemographic Institute, University of Indonesia. Currently, he isat IIASA (International Institute for Applied Systems Analysis),Austria as a postdoctoral fellow. Muhidin holds a Ph.D. indemography from the Faculty of Spatial Science, University ofGroningen, the Netherlands. His research areas are formaldemography, multiregional/multistate analysis, population

projections, and migration. His most recent publication is a book, based on his Ph.D. research, entitled “The Population ofIndonesia. Regional demographic scenarios using a multiregionalmethod and multiple data sources.”

ChotibSiew-Ean Khoo

Salut MuhidinZhou Hao

S.K. Singh


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Zhou Hao , now is a post-doctor of Department of Sociology,Peking University. He holds a B.A. in Demography fromHangzhou University (1997), and a PhD in Demography fromPeople’s University of China (2000). His research interests

include migration, urbanization, fertility analysis, populationdevelopment, population aging in China. Recently, most ofZhou’s research is connected to issues of migrant children, andthe relationship between migration and family structure. He alsoteaches statistics, migration, and demographic methods.

S. K. Singh is a lecturer in the Department of Public Health andMortality Studies at the International Institute for PopulationSciences (IIPS). He holds a Ph.D in Statistics/Demography fromBanaras Hindu University in 1990. S. K. Singh has extensiveresearch experience and his areas of research include rural-urbanmigration and return migration, assessment of women’s status,fertility, family planning and health, and infant and childmortality, and stable population theory. He is recentlyresearching on knowledge about HIV/AIDS and risk behaviouramong migrants in some selected developments projects. He hasalso published a number of book chapters and journal articles.


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Published by

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© Asian MetaCentre for Populationand Sustainable Development Analysis

Asian MetaCentre Research Paper Series No.8January 2003

The Asian MetaCentre’s primary aim is to advance understanding ofpopulation and sustainable development issues in the Asian context,particularly those related to population-environment interactions,population forecasting, and migration and families. The AsianMetaCentre Research Paper Series is a forum for the presentation ofpopulation and sustainable development research material byscholars working on a range of diverse issues in the Asian context.The series is intended to stimulate and facilitate scholarly andprofessional communication and interaction amongst interestedindividuals, universities and research institutions - local, regional andinternational. To accomplish these aims, the Asian MetaCentrewelcomes high quality research materials by the scholars andresearchers in the Asian Population Network.

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i

Content

Contents i

List of Tables ii

List of Figures iii

Chapter 1 1 Age Pattern of Migration from and into DKI

Jakarta, Indonesia: An Analysis of the 1995Intercensal Population Survey

Chotib

Chapter 2 26 Marrying and Migrating to Australia: AsianSpouses in Intra-and Inter-Cultural MarriagesSiew-Ean Khoo

Chapter 3 46 Migrated Household in Indonesia: An Explorationof the Intercensal Survey DataSalut Muhidin

Chapter 4 66 Migration and Household Characteristics:Evidence from ChinaZhou Hao

Chapter 5 84 Some Probability Models for Estimating thePropensity of Dependants’ Migration in Indiaand Their ApplicationsS.K. Singh


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List of Tables

Table 1

Table 2

Table 3

Table 4

Table 5Table 6

Table 7

Table 8

Table 9

Table 10Table 11Table 12Table 13Table 14Table 15Table 16Table 17Table 18

Table 19

Table 20

Table 21

Table 22

Parameter Values of Out-Migration from Jakarta with Full Model(11 Parameters)Parameter Values of In-Migration into Jakarta with Full Model(11 Parameters)Asian-Born Migrants in the Spouse or Prospective Spouse Visa Categories,1993-1995: Distribution by Country of BirthBackground Characteristics of Migrant Spouses by Type of Marriage andSexSettlement Outcomes at 6 Month (W1) and 18 Months (W2) after ArrivalMaximum Likelihood Estimates of the Effects of Migrant Characteristics onEmployment Status at 6 Months (W1) and 18 Months (W2) after ArrivalOperational Definitions of Variables Considered for the Analysis ofMigrated Households, Indonesia, 1995Bivariate Analysis of the Selected Characteristics for Migration Status ofHouseholds, Indonesia, 1995Logistic Regression Analysis for being a Migrated Household, Indonesia,1990-1995Independent Variables Used in AnalysisDistribution of Household with Migrant in Urban and Rural AreaAverage Size of HouseholdAverage Number of Adult and Children in a HouseholdAverage Number of Migrant in a HouseholdLogistic Regression Result (Total Population)Logistic Regression Result (Male)Logistic Regression Result (Female)Distribution of Households According to Number of Migrants (Males agedfifteen years and above in different types of villages)Distribution of Households According to the Number of Migrants (Malesaged fifteen years and above) in Different Caste GroupDistribution of Households According to Number of Male Migrants (agedfifteen years and above) in which the Number of Male Migrants is Less Thanor Equal to the Number of Dependant Migrants in Three Types of Villages,viz, Semi-Urban, Remote and Growth-CentreDistribution of Households According to Number of Dependant Migrantsfrom a Household with at Least One Male Migrant Aged Fifteen and Above,in Three Types of Villages, viz, Semi-Urban, Remote and Growth-CentreJoint and Marginal Probabilities of i Male Migrants Aged Fifteen Years andAbove and n Dependants to be Migrated from a Household in Three Typesof Villages

13

18

31

34

3741

56

59

63

717273747577787989

90

94

95

96


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iii

List of Figures

Figure 1Figure 2Figure 3

Figure 4Figure 5Figure 6Figure 7Figure 8Figure 9Figure 10Figure 11Figure 12Figure 13Figure 14Figure 15

Figure 16Figure 17Figure 18

The Full Model of Migration ScheduleAge Pattern of Out-Migration from JakartaAge Pattern of In-Migration into Jakarta

Age Pattern of Male Out-Migration from JakartaAge Pattern of Female Out Migration from JakartaAge Pattern of Out-Migration from Jakarta into Urban AreasAge Pattern of Out-Migration from Jakarta into Rural AreasAge Pattern of Jakarta Born Out-Migration from JakartaAge Pattern of Non-Jakarta Born Out-Migration from JakartaAge Pattern of Male In-Migration into JakartaAge Pattern of Female In-Migration into JakartaAge Pattern of In-Migration into Jakarta from Urban AreasAge Pattern of In-Migration into Jakarta from Rural AreaAge Pattern of Jakarta Born In-Migration into JakartaAge Patten of Non-Jakarta Born In-Migration into Jakarta

Migrants in Intra- and Intermarriages: Distribution by Birthplace of SpousesProportion of Migrants by Migration Triggers, Indonesia 1980-1985Proportion of Migrants by Migration Triggers, Indonesia 1990-1995

82222

222222222323232323232424

324950


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Chapter 1

Age Pattern of Migration from and into

DKI Jakarta, Indonesia: An Analysis ofthe 1995 Intercensal Population Survey

ChotibDemographic InstituteFaculty of Economics

University of Indonesia

Abstract

Age-specific fertility and mortality rates have been shown to exhibit remarkably persistentregularities. Consequently, demographers have found it possible to summarize and codifysuch regularities by means of mathematical expression. Although the development of themodels of fertility and mortality schedules has received considerable attention in demographicstudies, the construction of models of migration schedules has not been much developed. Yet,the techniques that have been successfully applied to estimate fertility and mortality schedulescan be readily extended to deal with the migration schedule.

This research examines age-sex specific migration patterns into and out of DKI Jakarta basedon SUPAS 1995 (1995 Intercensal Population Survey). The estimation is carried out in linewith the multi-regional demographic approach, which only considers out-migration. In-migration to region B from region A is simply out-migration from region A to region B.Specifically, it examines the out-migration from DKI Jakarta to the rest of Indonesia and theout-migration from the rest of Indonesia to DKI Jakarta. Another feature of this research is theexistence of control variables (gender, urban-rural characteristics, and place of birth) in theestimation of the migration pattern.

It demonstrates that out-migration from DKI Jakarta (to the rest of Indonesia) is more “ childdependent ”, whereas in-migration (out-migration from the rest of Indonesia) to DKI Jakarta is

more “labour dominant ”. The research also finds that the intensity of female migrants ishigher than the intensity of male migrants; the intensity of urban to urban migration is higherthan the intensity of urban to rural or rural to urban migration; and the propensity to move outof DKI Jakarta is three times as high for migrants who were born outside DKI Jakarta than for


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those born in DKI Jakarta. Similarly, the propensity to move out of the rest of Indonesia isalmost seven times as high for migrants who were born in DKI Jakarta than for those born inthe rest of Indonesia.


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Introduction

akarta, the capital city of Indonesia, is at the same time the centre of trade and business,government, and culture and is always regarded by the population as the place where they

can improve their living standards. It is not surprising that the rate of migration into JakartaCapital City is very high. In the period of 1975–1980, the number of recent migrants to DKIJakarta was 766,363, mostly from Central Java (34.3%) and West Java (27.6%). The numberof recent migrants from DKI Jakarta was 382,326, mostly to West Java (64.6%) and CentralJava (9.8%) as their places of destination. Hence, DKI Jakarta had the biggest net positivenumber of recent migrants, with about 384,037 people.

However, since 1990, the pattern has been reversed. In the period of 1985–1990, the totalnumber of migrants to DKI Jakarta was 833,029, mostly from Central Java (40.5%) and WestJava (25.6%). Apparently, the number of out-migrants was as high as 933,377 with West Java(70.0%) and Central Java (12.2%) as the main destinations. As a consequence, DKI Jakarta

had a negative net migration rate of 100,348 people (Firman, 1994; Chotib and Permadi,1994). These data obviously indicate that DKI Jakarta has shifted from being a receivingdestination to a migrant supplier.

This change is not without the role played by West Java Province as a place of destination formigrants from DKI Jakarta. This is particularly true when the JABOTABEK (Jakarta–Bogor– Tangerang–Bekasi) geographical concept was launched on 3 January 1974 in an effort to finda solution for the population problems of DKI Jakarta. This concept is no other than a policyon spatial distribution of the city planning of Jakarta City, which is trying to disperse newactivities as well as some of those already in operation within the new developing areas. Itwas expected that these new areas would be able to attract the population of Jakarta to livethere. New jobs would be created along with regional transportation network andinfrastructures in designated centers of settlement (Suselo, 1977).

The expectation became a reality as shown by the change of migrant destination betweenJakarta and West Java in 1980 to 1990. The 1995 Intercensal Population Survey dataillustrates the continuing trend of migration to and from DKI Jakarta during the period 1990– 1995. This data show that between 1990 and 1995, the number of recent migrants to DKIJakarta was 594,542 people, while the number of out-migrants was far higher at 823,045

people (Firman, 1988). These data reveal that DKI Jakarta in 1990–1995 had a negative rateof net migration of over 228,000 when compared to 1985–1990.

Data from the 1995 Intercensal Population Survey reveals that a bigger number of out-migrants from DKI Jakarta went to West Java (65.74%), Central Java (12.20%) and 7.09% toEast Java (Central Bureau of Statistics, 1997). Most of those whose destination was West Javawere settling in the peripheries of Metropolitan Jakarta known as JABOTABEK. They weredrawn to these areas by the development of new settlements and availability of employment

J


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opportunities especially in the industrial sectors. At the same time, those in-migrants to DKIJakarta were mostly from Central Java (34.08%), West Java (31.00%) and East Java (9.97%)(Central Bureau of Statistics, 1997). These indicate that the provinces in Java dominate boththe generating regions and the destination regions. The analysis of the 1990 PopulationCensus by Firman (1994) shows an almost similar pattern.

The rising regionalism in Indonesia has necessitated the use of an analytical method such asmulti-regional demography in order to be better able to understand Indonesian populationdynamics and their socio-economic-political implications. It is, therefore, important to furtherexamine the age-sex pattern of migration from and to DKI Jakarta. This study aims toestimate the age-sex migration pattern, controlled by gender, rural-urban residence, and placeof birth. The result of this estimation will be beneficial for multi-regional demographicanalysis involving DKI Jakarta and the rest of Indonesia.

Data and Method

This study uses 1995 Intercensal Population Survey (SUPAS, 1995). Migration status fromSUPAS 1995 is defined from the answers to four questions: current province of residence,

province of birth, province of last place of residence, and province of residence five yearsearlier. ‘Province’ is therefore the unit of analysis. Hence three types of migration areidentified as follows:

1. Lifetime migrant is someone whose current province of residence is different from hisor her province of birth.

2. Recent migrant is someone whose current province place of residence is different fromhis or her province of residence five years earlier.

3. Total migrant is someone whose current province of residence is different from his orher last province of residence.

Among the three types of migration, recent migration often appears in many discussions onmigration. This type of migration tends to reflect the population mobility dynamics in theshorter term than that of other types.

The dependent variable is migration rate and the main independent variable is the age ofrespondent. This rate is obtained from the proportion of the population who were stated asmigrants for each year of age of respondents. Therefore, the variables selected from theSUPAS 1995 raw data were current province of residence, province five years earlier, and ageof respondents. The age-specific pattern was then controlled by migrant characteristics suchas gender, province place of birth, and urban/rural area of origin/destination at currentresidence and residence five years earlier.


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The index summarizing the ASMR (Age-Specific Migration Rate) is called GMR (Gross-Migraproduction Rate), calculated by the formula:

(1)

where x = yearly age of migrants.

The estimation of the parameters of the migration schedule is calculated by FORTRAN programming. The estimation is to find “the best” value of function parameters figured out bythe schedule based on the least square method. This method is formulated as follows:

(2)

where:

(3)

and ( ) x M ˆ is ASMR(x) based on empirical data, where x = yearly age of migrants.

The procedure mentioned above is intended to obtain θ = (a 1, a 2, a 3, α1, α2, α3, λ2, λ3, µ2, µ3, c)which minimizes the Q. If θ can not be obtained directly, then iteration is used as stated

below:

(5)

where βi is “step size”; d i is direct of difference (negative or positive value); and i is numberof iteration. The iteration will be stopped when:

(6)

with ε>0.

Measurements in Migration

The multiregional approach treats the national population as an interaction system of anumber of sub-national populations. Populations who were born in a region (sub-nationalregion) has the risk to move to other places. Rogers (1995) has extended the theory of

∑= x ASMRGMR

[ ]∑ −= 2),(ˆ),()( θ θ θ x M x M Q

{ } c x

e xa x

e xa x xa x M +−−

−−−+−−

−−−+−=)

3(

3)3(3exp3)

2(

2)2(2exp2exp1)(µ λ

µ α µ λ

µ α α

iiii d β θ θ +=+1

ε θ θ p−+1i


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multiregional projection and established a theoretical method of multiregional populationanalysis, using data on migration, in addition to those on deaths and births.

The fundamental differences between the uniregional (conventional) and the multiregionalapproaches to population analysis may be illuminated by imagining an interconnected systemof a number of regional populations, linked to the each other by migration flows. In otherwords, a regional population changes interdependently with all other remaining regions withina country.

In this system, the migration outflows from the rest of the country define the migration inflowof a region in the country. The uniregional analysis of the population changes in all of regionsfocuses on the changes in the outflows and inflows in each region, one at a time. On the otherhand, the multiregional perspective regards all of regions as a system of inter-regionalinteracting populations, with a pattern of outflows. Moreover, the multiregional approachemploys rates of flow that refer to the appropriate at-risk populations; the uniregionalapproach cannot do that because it considers only a single population at risk for both out-migration and in-migration.

The measurement of mortality rate often deals with the expected duration of time. Themeasurement e 0 describes the duration of life expectancy that would be lived out by the

population since they were born, if there were no change in the age specific death rates.Similarly, e 65 is the life-expectancy rate that would be lived out by people after they reach theage of 65.

The measurement of fertility rate is principally about the expected number of persons (thatwere born) or number of births. Measurements such as Total Fertility Rate (TFR) or GrossReproduction Rate (GRR) show the number of births by women during their reproductiveage. In the GRR, the number of baby girls is specially identified. Fertility is a repetitive eventin the life of human beings (in this case the life of the women of reproductive age).

If these women, who have the propensity to give birth are “permitted” to die, then theinteraction between mortality and fertility will produce a measurement called NRR (NetReproduction Rate) a continuation of GRR. It differs from GRR in that the GRR starts withfemale population age 15 (the beginning of reproductive age), and that they have to remainalive until the end of their reproductive age, while the NRR will start with 1,000 baby girls(female population at age 0) and these baby girls might die before they reach the end of theirreproductive age (Ananta, 1990).

The occurrence of migration can be measured like mortality, that is within the context of the

expected duration of time. Hence we will be able to estimate the length of time a person will be living in a certain location. But unlike mortality which happens only once in a lifetime,migration can happen many times within the lifetime of a human being. Therefore, migration


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is also considered similar to fertility, which is a repetitive event. As a result, migration can becalculated by applying the expected number of migrations per person.

The most simple and rough estimate of migration is the Crude Migration Rate (CMR),showing the number of migrants among the total population in a certain area. This is a veryrough calculation because it does not include people who are at ‘risk’ of migration behavior.To reflect the age-sex selectivity, migration is often measured by ASMR (Age SpecificMigration Rate), indicatin g the number of migrants of a certain age specific among peoplewho are at ‘risk’ of being migrants at that particular age.

To summarize the ASMR of various age groups that are available, an index called the GMR(Gross Migra-production Rate) is applied. The GMR is analogous to GRR in the fertility rate.This index shows the intensity of out-migration from a certain location for a certain durationof time. For example, GMR=14 in 1993 denotes that a person will out-migrate about 14 timesduring his/her life time (that is until his/her mobile age) provided he/she is not “permitted” todie during that age and he/she always follows the pattern of ASMR available in 1993. If

people who are at risk of migration are ‘permitted’ to die, then the index will be a NMR (NetMigra-production Rate) which is analogous to NRR in a fertility rate.

A Review on Model of Migration Schedule

It has been described above that out-migration is very selective with respect to age and sex.Empirically the male population has the propensity to be more mobile than the female

population. In terms of age selectivity, the young population (those below age 20) generallyshows a high rate of migration, while those in the middle age group has the lowest rate ofmigration. The rate of children migration usually reflects the rate of the parent migration.Therefore, the migration rate of children also reveals a high figure. Meanwhile, thedestination of migration tends towards regions with a cool climate or cities that providesufficient social services, showing a retirement peak of people in the early sixties (Rogers,1984; Rogers and Castro, 1984).


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Figure 1 The Full Model of Migration Schedule

Figure 1 illustrates the regularity of migration by age group. It shows the presence of aretirement peak as full model of a variety of available schedule models. It also shows a brokengraphic indicating a decomposition of a continuous graphic. There are four kinds of brokengraphics identified in this figure. They are:

1. Pre-labour force, shown by an exponential curve with decreasing rate of a 1.

2. Labour force, shown by a curve with one peak of mean age µ2 and an increasing rateof λ2 and decreasing rate of α2.

3. Post-labour force, a curve of a bell-shape showing a mean age rate of µ3, an increasingrate of λ3 and a decreasing rate of α3.

4. A c constant needed to revise the mathematical accuracy of the schedule estimate


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The summation of four graphs is formulated as follows:

{ } { { ce xae xa xa x M x x +−−−+−−−+−= −−−− )(333)(22211 3322 )(exp)(expexp)( µ λ µ λ µ α µ α α (7)

where: x = 0, 1, 2, …

The above equation shows a model of a full migration schedule comprising 11 parameters.These parameters represent the following:

1. Parameters indicating level migration, namely a 1, a 2, a 3, and c. A change in GMR willmodify this parameter group, but it is not likely to change the other seven parameters.

2. Parameters indicating profile of migration, namely α1, α2, α3, λ2, λ3, µ2, and µ3. Thechange of profile will modify these seven parameters, but they will not necessarilychange the other four parameters.

Some interesting phenomena indicated in Figure 1 include the three special points in themigration profile by age group, namely:

1. xl, the lowest point of migration rate of pre-labour force age. The migration rate or M(x) at this point is usually the lowest

2. xh as a low point. It is a point which produces the highest M(x) of labour force age. Atthis level M(x) becomes the highest point compared to other points even though it isoutside the labour force age.

3. xr is the highest point of the post-labour force age. It is lower than xh.

Three interesting phenomena are drawn from these three special points, namely:

1. “Labour force shift” X=x h – x l which is an age difference between the lowest point andthe highest point. or the years needed from xl to xh.

2. “Jump” B, which is a difference between M(x) produced by xl and xh.

3. “Parental shift” A reflects a close relationship between children migration and parentalmigration. The rate is adopted by finding the difference between the M(x) rate of pre-labour force and labour force age groups. The mean difference of the rate of these twoage groups for a M(x) is called A (parental shift).

The characteristics of the migration schedule can also be seen in the relation between the pre-labour force group and the labour force group. A schedule is considered to have an initial


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peak, when µ2 is smaller than 19. It is a normal peak if µ2 is between the age of 19 and 22.The schedule is considered to have a slow peak if µ2 is above 22 years old.

A schedule is called to have a labour dominant if δ12 = a 1/a 2 is less than or equal to 1/5. If theδ12 is above 1/5 up till 2/5, the schedule is considered normal. When δ12 is above 2/5, theschedule is called ‘child dependant’.

Another parameter is called labour asymmetry, which shows a distorted peak of a curve oflabour force migration. The rate is notated by σ2=λ2 /α2. If σ2 is less than 2, then the schedule iscalled symmetric. A schedule is called normal asymmetric if σ2 has a rate of 5 and over. Thesame case can be applied to the peak of migration of old age group ( σ3 =λ3 /α3), which showsan index of retirement asymmetry. This index could also be defined and studied in ananalogous manner. If σ3 is less than 2, then the schedule is called symmetric. It is normalasymmetric if σ3 is more than 5.

The c parameter is to increase or decrease the total migration level. The c parameterrepresents the other variables, which are not defined in the model but they may influence theintensity of migration.

Based on the migration pattern of pre-labour force curve, Rogers (1984) classified theschedules into three types of model, namely:

1. Full model. This model is shown by the equation 1 and Figure 1.

2. Model without peak. This model has no peak for post-labour age group. The curvecreated at the post-labour force is an exponential curve upwards. Hence the

mathematical equivalence is as the following:{ } { ( ) c xae xa xa x M x ++−−−+−= −− 33)(22211 exp)(expexp)( 22 α µ α α µ λ (8)

3. Simple model. This model has no age pattern for the post-labour force. Hence themathematical equivalence is as follows:

{ } ce xa xa x M x +−−−+−= −− )(22211 22)(expexp)( µ λ µ α α (9)

The model is applied to DKI Jakarta and the rest of Indonesia. However, to focus on DKIJakarta, the following section groups the discussion into “migration out of DKI Jakarta” and“migration into DKI Jakarta”, with “migration into DKI Jakarta” seen as equivalent to“migration out of the rest of Indonesia”.


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Migration Out of DKI Jakarta

Without Control Variables

Table 1 shows that GMR out of DKI Jakarta is 6. This figure indicates that the population ofDKI Jakarta will out-migrate 6 times during their lifetime if they are not “permitted” to die before their mobile age, with the assumption that the migration pattern by age group followsthe pattern available during 1990–1995.

A comparison of the parameter rates of a 1, a 2, a 3 indicates an increasing migration from the pre-labour force age group into the labour force age group and decreasing migration into the post-labour force age group. Table 1 shows a 1a 3, where a 1 is the migration level of pre-labour force age group; a 2 is the migration level of labour-force age group, and a 3 for post-labour force age group.

The other seven parameters like α1, α2, µ2, λ2, α3, µ3, and λ3 have an effect on the schedule pattern. The change of pattern is not always followed by a change of level or intensity ofmigration (GMR). This pattern refers to whether the curve is symmetrical or asymmetrical. Tomeasure whether the curve is symmetrical or not, it is indicated by the ratio between λ and α as described by σ, known as labour force asymmetry for σ2 and retirement asymmetry for σ3.

Table 1 shows the value of σ2=10.77, which means that the curve of labour force age group isdefinitely asymmetric. The increase of migration rate towards the peak age (starting from xl to

xh) is so steep compared to its decline. The increase of migration rate is so fast and hasreached a difference of 0.07 within a period of nine years (see parameter X ) beginning at the

age of 15 until about 25 years old. Between these two age groups there is a concentrated out-migration with an increasing number. This phenomenon can also be seen in the mean age ofthe labour force ( µ2), which is 19 (that is, between 15 years and 25 years old).

On the other hand, with the post-labour force age group, the decrease of migration rate is evenquicker than the increase as shown by the value of σ3 of 0.15 (less than 1). The peak ofmigration will occur when these groups reach the age of 58 with a rather slightly slopingincrease and then decreasing more quickly until it slows down again at the end of their mobileage. Therefore the mean age of the migrants of this group is 77 years. It is at the far right ofthe curve peak.

Another parameter derived from the estimate of model parameter is A, which depicts a parental shift, and reflects the mean age difference between adult migrants and childrenmigrants by the same migration rate. As mentioned before, child migration is a reflection of

parental migration. The estimate result shows that the A value is almost 31 year. This means


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that the mean difference between the age of the parents and the children they brought whenthey were migrating is 31 years.


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Table 1 Parameter Values of Out Migration from Jakarta with Full Model (11 parameters)By Sex By Type of Destination

Parameter All ofMigrants

Male Female Urban Rural

GMR (data) 5.995650 5.743720 6.170010 4.110370 2.076909GMR (model) 6.004874 5.749329 6.168259 4.109458 2.091313a 1 0.1365 0.1600 0.1710 0.0925 0.055α1 0.0518 0.0491 0.0352 0.0321 0.113a 2 0.1371 0.1820 0.0922 0.0855 0.083α2 0.0330 0.0374 0.0056 0.0275 0.094µ2 19.4842 21.0963 17.4714 19.4645 21.683λ2 0.3553 0.2815 0.8461 0.3271 0.254a 3 0.0001 0.0001 0.0001 0.0000 0.000α3 0.6769 0.6849 9.0038 0.6848 0.685µ3 77.1983 74.3823 60.2773 76.4264 77.150λ3 0.1017 0.1018 1.1793 0.1020 0.102c 0.0017 -0.0144 -0.0380 -0.0108 0.0109Mean age (data) 36.373784 33.929516 38.512938 36.150872 36.384743Mean age (model) 36.485950 33.965714 38.536428 36.142740 36.789072σ2 10.767143 7.522860 51.448188 11.915267 2.68612σ3 0.150273 0.148568 0.130980 0.148932 0.14924δ12 0.994978 0.879189 1.855902 1.082001 0.66648δ32 0.000607 0.000279 0.001419 0.000560 0.00040

xl 15.379876 15.938934 15.652245 14.863987 16.25107 xh 24.854771 26.910346 21.115083 25.747583 24.21867 xr 57.963349 54.879523 58.553212 57.083785 61.11351X 9.474895 10.971412 5.462838 10.883596 7.96759A 30.822764 32.868129 28.849299 33.179814 27.57081B 0.073000 0.086982 0.068168 0.044669 0.03328


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Controlled by Sex

As far as intensity is concerned, it is obvious that the rate of migration of the female population is higher than that of the male population. It is shown by the rate of GMR, whichis 6.2 for women and 5.7 for men. The difference also occurs in the mean age of the labourforce group. The female migrants are mostly younger than the male migrants (17 and 21 yearsold respectively). Similarly with the post-labour force group where the mean age of femalemigrants is 60 years, while male migrants’ mean age is 74 years. The lower mean age offemale migrants is thought to be the result of being married (as wife), and they are mostlyyounger than their husband (the male migrants). In general the reason for women to migrateout of DKI Jakarta is the family.

However, when looking into the rate in general, the mean age of female migrants tend to beolder than that of the male migrants. The mean age of female migrants is 38.5 years, whilemale migrants’ mean age is 34 years. The high mean age of female migrants is very muchaffected by the high rate of migration at the peak curve of female post-labour force. This

phenomenon is not common because the peak of the post-labour force group is usually lowerthan the peak of the labour-force group. The great numbers of old women who migrate out ofDKI Jakarta is an interesting phenomenon to be further analyzed.

The curve of female migrants is very asymmetric as shown by the value of σ2, which is 51,and far above the value of male migrants, which is σ2=7.5. This shows that the increase ofmigration rate towards the peak age of the labour force is higher in female migrants than inmale migrants. The length of labour force age ( X ) for females is also 5 years shorter, startingfrom age 16 to age 21 years, while the X rate of male migrants is 11 years longer, between 16and 27 years.

Controlled by Urban-Rural Residence

Urban or rural areas are used to classify the place of destination of the migrants from DKIJakarta to outside DKI Jakarta. Quantitatively the GMR to urban areas is 4 while the GMR torural areas is 2. The higher intensity of migrants to urban areas is the result of the emergenceof new settlements and industries in the peripheries of DKI Jakarta (they belong to West Java

province). This is a sign of faster urbanization in the DKI Jakarta peripheries.

The throng of people moving to urban areas is mostly from the labour force age group. Theincrease of migration rate of this group is so steep as indicated by σ2=12 with a mean youngerage as well ( µ2=19 years), while the rate of rural areas is σ2=3 and µ2=12 years. The range oflabour force curve ( X ) to urban areas is also longer than the one to rural areas. X means thedifference between age when migration rate is highest and age when migration rate is lowest


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in the labour force curve. Table 1 shows that the value of X to urban areas is 11 years, whileto rural areas is 8 years.

The dominance of labour force age group migrating to urban areas is also indicated by thehigher curve peak than the one to rural areas. The peak of migration to urban areas is almost0.1, while the one to the rural areas is a little above 0.05. Moreover, the start of increase of themigrants to urban areas is at a younger age (15 years) than to rural areas (16 years) althoughthe peak age curve of the labour force group to urban areas is older (26 years) than to ruralareas (24 years).

If young migrants are a little dominant in the urban areas, the old migrants are very dominantin the rural areas. The mean age of post labour force group in rural areas is 77 years, one yearolder than in the urban areas. Similarly, the peak curve of the post-labour force group ofmigrating out to rural areas is 61 years, and the peak for migrating out to urban areas isyounger, at 57 years old.

Controlled by Place of Birth

Using place of birth as a variable in migration analysis is a new dimension in the study ofmigration behavior (Ledent and Termote, 1992). The dimension is reflected in themultiregional analysis of population projection, which simultaneously integrates the threedemographic components (fertility, mortality, and migration). With the inclusion of place of

birth and migration behavior, the life expectancy rate is no more analyzed uniregionally. Thedeath rate index is no more an indication of the length of time a population is expected to live,

but it rather emphasizes on where they will spend their life. The same also applies to fertility.The rate of fertility does not solely concern with the number of children delivered by amother, but where a mother was giving birth which essentially is related to the life expectancyrate and migration.

The difference of out-migration intensity from Jakarta by migrants’ place of birth is examined by Ledent and Termote (1992) in an analysis about migration from DKI Jakarta to outer provinces using the data of 1980 Indonesian Population Census. The analysis shows that theMGR for non-native Jakartanese migrants (born outside Jakarta) is 1.36; almost double therate of native migrants (born in Jakarta) of 0.75.

Meanwhile Kao and Hayase (1997) reported that the same case has also happened inZimbabwe during the period of 1982–1992. They discovered empirically that migrants bornin rural areas have a much lower propensity to migrate from rural to urban areas than those

born in urban areas. On the other hand, the rate of migration from urban to rural areas is muchlower for migrants born in urban areas.


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The analysis with the 1995 SUPAS indicates that GMR out of DKI Jakarta for migrants bornoutside DKI Jakarta (10.5) is three times bigger than the GMR for migrants born in Jakarta(3.4). See Table 1. Therefore, the out-migration rate from DKI Jakarta for those born inJakarta is lower than the rate for populations born outside DKI Jakarta.

However, the pattern of child and labour dependants, shown by δ12

, does not differ much by place of birth variable. The result shows that both are more “child-depandant” than “labourdominant”. In other words the two groups of migrants are more dominated by the pre-labourforce age group especially those migrants who were born outside Jakarta.

The rate of parameter σ2 shows that the curves of both types of migrants are almost similar,with a rate of 3 for those born in Jakarta and 3.8 for those born outside Jakarta. Both havesimilar paces of increase for the labour force curve.

Migration Into DKI Jakarta

Without Control Variables

Contrary to out-migration, the in-migration to DKI Jakarta has relatively small migrationintensity as indicated by GMR of 0.2. This small GMR does not denote that the absolutenumber of migration is also small. The rate is small because the denominator is the rest ofIndonesia and many of them are far from DKI Jakarta. On contrary, the denominator of out-migration from DKI Jakarta is the DKI Jakarta itself, and the destination of migrants includesthose nearby DKI Jakarta.

Table 4 shows the in-migration pattern (profile) by age. The figure shows that the highest peak of migration appears when the labour force age is between 10 and 40 years. It alsoshows that the curve of the labour force group is symmetrical with σ2=1.9. This indicates thatthe increase of migration towards the peak age of the labour force curve is almost the same asthe decrease of the migration after the peak.

Another characteristic of out-migration is the relatively small migration level of pre-labourforce age group. Hence the δ12 value—which indicates the ratio between a 1 and a 2 —is sosmall that it is close to 0.07. The schedule of in-migration is more labour dominant than childdependant. This is natural since labour force age group who do not always bring their childrenor family along with them mostly dominates migration into DKI Jakarta. The migrants weremore economically motivated (looking for jobs) and therefore they have the propensity toleave their family behind. Some of them will occasionally return to their native placestemporarily, and some others do not, but they send remittances.


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Empirically the mean age of the migrants is 28 years. This age is much younger than the meanage of out-migrants, but the mean age of the labour force ( µ2) is relatively the same, at 19years. However, the beginning of the labour force curve for in-migration is earlier than theout-migration. The curve of out-migration started at the age of 15 with the peak at age 25, butthe curve of in-migration started at age 10 with the peak at age 21 years. People come to DKI

Jakarta at earlier age than when people leave DKI Jakarta.The mean age of in-migration for the curve of post-labour force group is almost similar to theout-migration (about 77 years). However, the curve for the post-labour force group does nothave any peak. This may indicate that DKI Jakarta is “not too attractive” for old age people.

Controlled by Sex

There is no difference in the pattern of in-migration into DKI Jakarta by sex. Both female andmale groups have the pattern of low migration intensity during their pre-labour force age,rapidly rising intensity in the labour force age, and slowing down intensity in the post-labourforce age. Yet, seen from the total population point of view, the intensity of the femalemigrants is relatively higher than that of male migrants. The GMR of the female migrants is0.2, higher than 0.18 for the male migrants. See Figure 11. The higher intensity in the totalfemale population than in the male population is because of the relatively large proportion offemales in the labour force group.

In general the mean age of the female migrants is two years younger than the age of malemigrants. The difference of five years also applies to the group of the labour force migrants.As shown in Table 2, µ2 for female migrants is 17 years and the µ2 for male migrants is 22years. Nevertheless, the mean age of female migrants for the post-labour force group is higherthan that of male migrants. In this group the mean age is 81 years for female migrants and 76years for male migrants.


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Table 2 Parameter Values of In Migration into Jakarta with Full Model (11 parameters)By Sex By Type of Original Place

Parameter All ofMigrants

Male Female Urban Rural

GMR (data) 0.207090 0.182514 0.212666 0.248387 0.174143GMR (model) 0.207085 0.182465 0.212665 0.248389 0.174143a 1 0.001597 0.001541 0.001180 0.003346 0.00381α1 0.075453 0.059860 0.062717 0.078598 0.25105a 2 0.022252 0.022074 0.025808 0.018012 0.02645α2 0.128929 0.146481 0.143464 0.094118 0.16052µ2 18.668815 22.193010 17.071280 18.344844 19.71459λ2 0.250957 0.202187 0.319129 0.286646 0.21144a 3 0.000000 0.000000 -0.000002 -0.000000 0.00000α3 0.687575 0.692203 0.670126 0.681632 0.52252µ3 77.648759 77.5587982 81.532215 77.553257 82.38789λ3 0.102400 0.101734 0.109193 0.103025 0.06711c 0.000346 0.000263 0.000519 0.000602 0.000117Mean age (data) 28.046984 29.640830 27.162742 30.699060 26.791715Mean age (model) 28.187347 29.793561 27.389420 30.816988 26.668253σ2 1.946468 1.380292 2.224444 3.045619 1.31719σ3 0.148930 0.146971 0.162944 0.151145 0.12845δ12 0.071769 0.069827 0.045725 0.185751 0.14433δ32 0.000014 0.000037 -0.000078 -0.000015 0.00000

xl 10.565128 11.845485 10.404852 11.919979 10.00928 xh 21.244200 23.698802 19.535518 22.043569 21.00317 xr 56.029463 58.049494 80.006974 65.252028 50.31356X 10.679072 11.853318 9.130666 10.123590 10.99388A 36.909934 36.861828 35.647470 35.247599 34.59530B 0.009025 0.008052 0.011209 0.008214 0.00970

Note: *) No logical value


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The analysis also finds a steeper increase of migration rate towards the peak age of the labourforce. The σ2 parameter for female migrants has the value of 2, and the peak migration rate isalso high, almost 0.013. However, the value of σ2 for male migrants is 1 with a lower peakmigration rate, about 0.009. Although the intensity is lower, the curve of the male labour force

tends to be wider, between 12 and 24 years, compared to the female migrants, who started thecurve between age 10 and 19 years.

Controlled by Urban-Rural Characteristics

There is no difference between curve profiles (patterns) of migration from rural areas andurban areas. However, there is a marked difference in the intensity by urban-rural residence.The intensity of in-migrants to DKI Jakarta from urban areas (with GMR=0.25) is higher thanthose from rural areas (with GMR=0.17). This result may seem to be in conflict with thefinding, also from the 1995 SUPAS data set, that more than 60 percent of migrants coming to

DKI Jakarta were from rural areas. It should be noted here that the intensity as measured byGMR is an age-standardized measurement. Because rural areas have a larger young agegroup, both in percentage and absolute number, the percentage of migrants entering to DKIJakarta is dominated by those from rural areas. However, the seemingly dominant ruralmigrants disappear as soon as we control the age composition by using GMR as themeasurement of migration.

A variation is also found in the migrants’ mean age. In general, the migrants from urban areasare older (31 years) compared with migrants from rural areas (27 years). On contrary, in post-labour force group, the mean age of migrants from urban areas is younger (76 years) thanmigrants from rural areas (83 years). There is no much difference in the mean age of migrantsamong the labour force group with 18 in urban and 19 in rural areas.

Controlled by Place of Birth

As discussed earlier, a person tends to live longer in his/her birthplace. The difference of in-migration to DKI Jakarta by place of birth supports this phenomenon. Migrants born in DKIJakarta have a greater propensity to return to DKI Jakarta—7 times higher than those bornoutside Jakarta. The GMR of those born in Jakarta is 1.3 times, and the GMT of those bornoutside Jakarta is only 0.19. A similar case was also found in the study by Ledent andTermote (1992), which shows that the in-migration intensity to DKI Jakarta of migrants bornin Jakarta is 6 times higher than of those born outside DKI Jakarta. In other words, people

born in DKI Jakarta are more likely to return to DKI Jakarta compared to those born in the“rest of Indonesia”.


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If the life expectancy rate of someone is decomposed into specific regions, it is apparent thatthere is a tendency for people to live longer in their native place, the place where they were

born (Rogers, 1995) This assumption/statement is confirmed by another study on migration behavior which includes place of birth as free variable, done by Kao and Hayase (1997).

The propensity for migration is higher for the migrants born in Jakarta, particularly among pre-labour force age groups. This phenomena is seen in the a 1 value—indicating the migrationlevel of pre-labour force group—which is higher for those born in Jakarta (0.35) than forthose born outside Jakarta (0.001). The very high migration level of the pre-labour forcegroup has also made the parameter value of δ12 higher for migrants born in DKI Jakarta (4.1),than those born outside Jakarta (0.06). Hence the migration of people born in Jakarta is morechild-dependent. On the contrary, migrants born in Jakarta have higher migration level for thelabour-force group than the pre-labour force group and therefore the migration profile is morelabour dominant.

This difference of characteristics shows that migrants born outside Jakarta are more

economically motivated and mostly dominated by the labour-force age group. Whereasmigrants born in Jakarta are more motivated by non-economic activities. According to thedata of SUPAS (Intercensal Population Survey) 1995, 32.62%—which is the biggest

percentage—of the in-migrants to Jakarta gave the reason of looking for jobs. This iscompared to migrants born in Jakarta, more than half of whom (52.73%) said they came toJakarta to join the family.

There is a great difference in the curve profiles of the two groups of labour-force migrants toJakarta. For the migrants born in Jakarta, the schedule does not produce a curve but a plateau.Small peaks emerge at age 21 and 45 years. This is the reason why the α2 value of thesemigrants has a negative rate (which means it will not increase again). This “strange” curve has

an implication on the value of A parameter which cannot be obtained by mathematical tools.On the other hand, the labour force curve of migrants born outside Jakarta looks like curves ingeneral, with a cone-shaped curve with a higher peak. This shows that the labour-force agegroup dominates migration.

In general the mean age of migrants born in Jakarta tends to be younger than that of migrants born outside Jakarta. The mean age of migrants born in Jakarta is 26 years, two yearsyounger than migrants born outside Jakarta. A similar result is found with the labour-forcecurve. The mean age of migrants born in Jakarta has µ2 value of 14 years, while the µ2 ofthose born outside Jakarta is 19 years. Another similar result is observed in the post-labourforce group, where the µ3 value of those born in Jakarta is 47 years, while the µ3 value of

those born outside Jakarta is much older, about 78 years.


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Conclusion

This study shows that the population group having a high probability to migrate consists of people aged between 15 up to 50 years with its peak at the age 20–25 years; female population; people who live in urban areas and those who live outside their places of birth.

The hypothesis of mobility transition mentioned by Zelinsky indicated that at the initial stage,the migration pattern is dominated by rural to urban movement. However, at the latest stages,migration is often dominated by interurban or intraurban variety and even migration fromurban to rural areas (Skeldon, 1990). Carr (1997) also mentions that in the developingcountries, patterns of internal migration are associated with the main urban metropolitanregions. Whereas in the advanced countries, the form of migration is often of the form ofmovement out from large cities into rural areas, and declining with distance.

This study has found that the intensity of migration is specifically high between urban areas.The intensity of out-migrants from DKI Jakarta is higher for those who went to urban areas.Similarly, the intensity of those going to DKI Jakarta is higher for those who come fromurban areas. The finding indicates that Indonesia has reached in the last stage of mobilitytransition, though it has not found urban to rural migration as observed in developedcountries.

The finding also indicates that out-migrants from DKI Jakarta are more “child dependant”,while those in-migrants are more “labour dominant”. This implies that DKI Jakarta is still anarea of destination, where people can come to improve their lives and standards of living. Onthe other hand, people leave DKI Jakarta because of non-economic reasons such as joining thefamily, housing problems, and educational opportunities.


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Figure 2 Age Pattern ofOut-Migration from Jakarta

Figure 3 Age Pattern ofIn-Migration into Jakarta

Figure 4 Age Pattern of MaleOut-Migration from Jakarta

Figure 5 Age Pattern of FemaleOut Migration from Jakarta

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.450.5

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Data Full Model

AGE

A S M R

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.450.5

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Data Full Model

AGE

A S M R

Figure 6 Age Pattern of

Out-Migration from Jakarta

into Urban Areas

Figure 7 Age Pattern ofOut-Migration from Jakarta

into Rural Areas

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Data Full Model

AGE

A S M R

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Data Full Model

AGE

A S M R

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Data Full Model

AGE

A S M R

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Data Full Model

AGE

A S M


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Figure 8 Age Pattern of JakartaBorn Out-Migration from Jakarta

Figure 9 Age Pattern of Non-JakartaBorn Out-Migration from Jakarta

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Data Full Model

AGE

A S M R

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Data Full Model

AGE

A S M R

Figure 10 Age Pattern of Male

In-Migration into JakartaFigure 11 Age Pattern of Female

In-Migration into Jakarta

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Data Full Model

AGE

A S M R

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Data Full Model

AGE

A S M R

Figure 12 Age Pattern of

In-Migration into Jakarta from Urban Areas

Figure13 Age Pattern ofIn-Migration into Jakarta from

Rural Areas

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Data Full Model

AGE

A S M R

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Data Full Model

AGE

A S M R


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Figure 14 Age Pattern of JakartaBorn In-Migration into Jakarta

Figure 15 Age Pattern of Non-JakartaBorn In-Migration into Jakarta

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Data Full Model

AGE

A S M R

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Data Full Model

AGE

A S M R


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References

Ananta, A. (1990) Parameter Penduduk Stabil (Stable Population Parameter) , Jakarta: Demographic InstituteFaculty of Economics University of Indonesia.

Carr, M. (1997) New Patterns: Process and Change in Human Geography , London: Thomas Nelson and Sons

Ltd.Central Bureau of Statistics (1997) Perpindahan Penduduk dan Urbanisasi di Indonesia: Hasil Survei Penduduk

Antar Sensus 1995 (Population Movement and Urbanization in Indonesia: The Result of Intercensal PopulationSurvey 1995) , Seri S4. Jakarta.

Chotib and Permadi, C. (1994) ‘Migrasi dan Kriminalitas Pinggiran Kota’, Migration and Crimes in Urban Fringe Area , in Kompas, 14 November.

Firman, T. (1994) ‘Migrasi Antarprovinsi dan Pengembangan Wilayah di Indonesia (Interprovincial Migrationand Regional Development in Indonesia)’, Prisma No. 7 Th XXIII, pp. 3–15.

——— (1998) ‘Migrasi dari dan ke DKI Jakarta (Migration from and into Jakarta)’, KOMPAS , 4 February.

Kao, Lee L. and Hayase, Y. (1997) Rural/Urban Migration in Zimbabwe in 1982–92: Selectivity by Gender, Place of Birth, and Education Attainment , Mimeograph.

Ledent, J. and Termote, M. (1993) Migration and Demographic Growth in Jakarta, 1975-1980: Analysis and Prospects , Discussion Paper No. 39-93, Montreal, Canada: INRS-Urbanisation.

Rogers, A. (1984) ‘Migration patterns and population redistribution’, in Andrei Rogers (ed.), Migration,Urbanization, and Spatial Population Dynamics , Boulder: Westview Press.

———— (1995) Multiregional Demography: Principles, Methods and Extensions , New York: John Wiley andSons.

Rogers, A. and Castro, L.J (1984) ‘Model migration schedules’, in Andrei Rogers (ed.), Migration,Urbanization, and Spatial Population Dynamics , Boulder: Westview Press.

Skeldon, R. (1990) Population Mobility in Developing Countries , London and New York: Belhaven Press.

Suselo, H. (1977) ‘Tinjauan Singkat Perkembangan Jabotabek (a review on Jabotabek Development)’, Prisma ,

5, pp. 23–31.


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Chapter 2

Marrying and Migrating to Australia:

Asian Spouses in Intra- and Inter-Cultural Marriages

Siew-Ean Khoo Australian Centre for Population Research

Research School of Social SciencesThe Australian National University, Australia

Abstract

This paper uses longitudinal survey data to examine the initial settlement experience of acohort of spouse and prospective spouse immigrants from Asian countries who are married toor migrating to marry Australian residents who are of the same or different cultural

background. It investigates their family circumstances and socioeconomic status during theirfirst two years of residence in Australia, and tests the hypothesis that migrant spouses inintercultural marriages adjust more quickly to Australian society than migrant spouses inintracultural marriages. It also compares the migrant spouses with other non-spouse migrantsfrom Asian countries to see whether there are differences in the settlement experiences of the

two groups.

The study shows that intermarried and intramarried migrants differ in several important waysin their socioeconomic and migration background that in turn resulted in differences in theiradjustment and settlement experiences after migration. It also confirms the hypothesis thatintermarried migrant spouses adjust more quickly to Australian society as indicated by their

better English language skills and greater participation in employment than intramarriedmigrant spouses.


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Introduction

amily migration is one of the three major components of Australia’s immigration program, the other two being skill migration and humanitarian migration. For the past

five years, since 1996, the number of immigrants in the family migration stream has beenabout equal to the number in the skill migration stream. Within the family migration stream,the largest component is the migration of spouses and prospective spouses. Each year, about16,000 men and women immigrate to Australia to join their spouses or prospective spouses.

Any Australian citizen or permanent resident aged 18 years or older can sponsor a spouse or prospective spouse for immigration. Unlike in the United States of America, sponsorship ofimmigrant spouses is not limited to citizens. There is also no limit to the number of spouseimmigrant visas that can be issued each year in Australia’s immigration program because ofits commitment to the objective of family reunion, although quotas have been introduced onthe number of prospective spouse visas. The definition of spouse includes a person who is

legally married to, or in a de facto relationship with, the Australian resident sponsor.Legislation passed in 1997 required de facto partners to have lived together for one year.Before 1997, a six-month cohabitation period was sufficient to qualify for immigration as aspouse.

Since the 1980s, a significant proportion of spouse migrants has come from Asian countries,either to be reunited with their Australian resident spouse or to marry their prospectivespouse. Some of the migrating spouses are married to or marrying partners of the samecultural background who have migrated earlier. Others are married to or marrying partnerswho are from a different cultural background, either native-born Australians or immigrantsfrom another part of the world.

Marriage and migration are major life cycle events. In most cases, each represents a change inlife style and in the case of migration, it is always associated with a change in residentiallocation. Each requires the individuals involved to adjust to these changes. For the men andwomen who are combining marriage and immigration, they have to make two types ofadjustment simultaneously—to a new partner and to a new country. During the mid-1990s,the Department of Immigration and Multicultural Affairs in Australia was concerned enoughabout the dual adjustment processes faced by immigrant spouses to produce an informationvideo and accompanying booklet called Marrying and Migrating to Australia to provide pre-migration information to migrating spouses. This video followed an earlier one aimed atmigrant spouses from the Philippines, which has been a major source of spouse migrationsince the late 1970s. The videos address adjustment issues faced by spouse and prospectivespouse immigrants. These issues can be quite different depending on whether the migrantspouse is in an intracultural or intercultural marriage.

F


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Immigrants who are reuniting with their spouse from the same cultural background may have been separated from that spouse for some years. They will have some catch-up to do in termsof adjusting to a new country as their spouse will have been resident in Australia for sometime. This will also be true of immigrants who are migrating to marry someone of the samecultural background, who has immigrated earlier. However, they will not have to adjust to a

different culture within the family, unlike immigrant spouses in intercultural marriages.Migrant spouses in intercultural marriages will have to adjust to a different culture within thefamily as well as to a new country of residence. However, their spouse, if native-born or has

been resident for a long time, may be in a better position to help them integrate into the widercommunity than a spouse in an intracultural marriage who may be a recent migrant.

This paper uses longitudinal survey data to examine the initial settlement experience of acohort of spouse and prospective spouse immigrants from Asian countries who are married toor migrating to marry Australian residents who are of the same or different cultural

background. It investigates their family circumstances and socioeconomic status during theirfirst two years of residence in Australia and tests the hypothesis that migrant spouses inintercultural marriages adjust more quickly to Australian society than migrant spouses inintracultural marriages. It also compares migrant spouses with other Asian migrants to seewhether their settlement experience differs from that of other Asian migrants and requiresspecial attention or assistance.

Spouse migration to Australia has been the subject of several studies. Many of these hadfocussed on the ‘mail order bride’ migration from the Philippines, which began in the late1970s and was characterized by the migration of Filipino women who were married toAustralian-born or European-born men. The studies had arisen partly from concerns about theincidence of marriage breakdown involving some of these women and there was some debateabout the success of these intercultural marriages (Cahill, 1990; Chuah et al. , 1987; Cooke,

1986; Smith and Kaminskas, 1992). There was also the related issue of ‘serial sponsorship’ offoreign-born spouses—the sponsoring of a succession of marriage partners after a breakdownin the relationship with the previous migrant partner(s)—which led the Department ofImmigration to commission a study of its prevalence and characteristics (Iredale, 1994).

The increase in spouse migration from a number of countries during the early 1990s also ledto an examination of the different contexts of spouse migration. Birrell (1995) suggested thatthere were four types of spouse migration to Australia. The first type is that of the ‘boy-meets-girl’ situation where the partners meet in the course of overseas travel, study or work. Thistype of spouse migration from Asian countries is usually associated with intermarriage. Thesecond type is that of family reunion following a period of separation when one partner had

emigrated earlier, often as a refugee. Some spouse migration from countries that have beensources of refugee migration such as Vietnam, Cambodia and Afghanistan has been of thistype. The third type is that of marriage migration where previous immigrants return to thehome country to find a marriage partner of the same cultural background and then sponsor


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their spouse’s migration to Australia. The fourth type is that of second generation Australiansreturning to their parents’ country of origin to find a marriage partner of the same ethnic

background. This tends to occur among immigrant groups that maintain a strong ethnicidentity. Khoo (1997; 2001) recently examined survey data on spouse migrants and theirsponsors and concluded that marriage migration was the predominant form of spouse

migration to Australia during the 1990s, followed by the ‘boy-meets-girl’ type of spousemigration, with only a small proportion being family reunion migration.

While these studies have focussed on the characteristics of spouse migration, it is not knownhow these migrants adjust to new marriage partners and a new country at the same time. Astudy by Penny and Khoo (1996) on immigrants intermarried with Australians suggested thatthe outcomes relating to personal identity and integration into Australian society variedenormously across individuals. Since their study focussed on intermarriage, it did not examinemigrants in intracultural marriages. This paper compares spouse migrants in intracultural andintercultural marriages in examining their adjustment to their new life in Australia.

Data and Methodology

The data for the study came from the first two waves of the Longitudinal Survey ofImmigrants to Australia (LSIA), conducted by the Department of Immigration andMulticultural Affairs. A sample of 5,193 immigrants was selected at random from the total

population of 75,000 immigrants who arrived during the period September 1993 to August1995 and who were the principal applicants for permanent resident visas, stratified by visacategory, State of residence and country of origin. The sample was first interviewed within 3to 6 months of their arrival in Australia. Interviews were conducted face-to-face by trainedinterviewers, with about one-third of the interviews involving bilingual interpreters. Therespondents were followed up one year later and re-interviewed. The attrition rate between thetwo interviews was about 15%.

Further details of the survey are available from the Department of Immigration andMulticultural Affairs (1997).

Included in the survey were 613 Asian-born men and women who were migrants in thespouse or prospective spouse visa categories 1. Data were missing for some variables ofinterest for one respondent. The remaining 612 respondents represented 15,732 Asian-bornimmigrants who were in the spouse or prospective spouse visa categories who arrived duringthe two-year sampling period, September 1993 to August 1995. Of the 612 Asian migrantspouses, 524 were re-interviewed in wave 2 of the survey, resulting in an attrition rate of 15%,

1 Other respondents in the sample excluded from this study are non-Asian-born migrant spouses and other types of migrantssuch as skilled migrants, refugees and other humanitarian migrants, and other family reunion migrants such as parents anddependant children.


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which was the same as for the total sample. A comparison of the characteristics of the 524respondents with the original sample of 612 did not show any attrition bias.

Data were collected in the survey about the immigrants’ socioeconomic circumstances beforemigration, the migration process and the immigrants’ family and socioeconomiccircumstances since arrival in Australia. Questions were also asked about their health statusand level of satisfaction with life in Australia.

Data were also available from the survey about the Australian resident spouses of thesemigrants: whether they were born in Australia or overseas, and if overseas-born, their countryof birth and when they migrated to Australia. From information about the country of birth, itwas possible to infer whether the couple was in an intracultural or intercultural marriage.

For the purpose of this study it was assumed that the migrant spouse was in an intraculturalmarriage if the sponsoring spouse was born in the same country or the same region. Forexample, if a migrant spouse was born in China and the sponsoring spouse was born in China,Hong Kong or Taiwan, it was assumed that the marriage was intracultural. If the sponsoringspouse was born in Australia or an overseas country that was not within the Asian region (forexample, Europe or North America or New Zealand), then the marriage was assumed to beintercultural. For example, some Philippines-born women were married to men born inEurope; these were categorised as intercultural marriages.

The migrant spouses were examined according to the following aspects of their settlementexperience: family situation, economic participation, economic wellbeing, health andsatisfaction with life in Australia. The family circumstances of the migrants were examined

by looking at their marital status, number of children and housing arrangements (for example,whether they were living with other relatives) and changes in these circumstances between thefirst and second interviews. Economic participation was indicated by their employment status

while economic wellbeing was examined in terms of their reported weekly income andhousing occupancy status.

Comparisons were made between migrants in intracultural marriages and those in interculturalmarriages. The spouse migrants were also compared with other Asian migrants in the survey(migrants in other visa categories) to see in what ways spouse migrants differed from non-spouse migrants in their migration and settlement experiences.

Where significant differences in outcomes were observed between intramarried andintermarried migrants, multivariate statistical analyses were carried out to investigate thefactors correlated with positive or negative outcomes. Since the outcome variables wereusually dichotomous, loglinear analysis using the CATMOD procedure in SAS was themethod used in the multivariate analyses.


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Spouse Migration from Asian Countries

A number of Asian countries are among the major sources of spouse migration to Australia.While the largest number of spouse migrants has usually come from the United Kingdom,three Asian countries were also sources of large numbers of spouse migrants during the 1980s

and 1990s . They were China, Vietnam and Philippines. Together with the UK, they wereusually the top four sources of spouse migration to Australia (Khoo, 2001). China was brieflythe top source country in 1995 to 1996 when Chinese nationals who had been temporaryresidents after the Tiananmen Square incident in 1989 were granted permanent resident statusin November 1993 and were able to sponsor family members for immigration. Table 3 showsthe country of origin of the Asian-born spouse migrants in this study. The largest number ofspouse migrants came from Vietnam, which accounted for one-quarter of all Asian-bornspouses who migrated during the two-year period of the survey. Large numbers of migrantspouses also came from Philippines, China and India. These four countries accounted fornearly two-thirds of all Asian-born spouse migrants during the years 1993 to 1995.

Table 3 Asian-Born Migrants in The Spouse Or Prospective Spouse Visa Categories, 1993-95: Distribution by Country of Birth

Birthplace Number ofrespondents

Weighted population

Intra-culturalMarriage

(%)

Female(%)

Cambodia 36 486 91.7 78.6Indonesia 44 522 51.1 70.0Malaysia 37 519 60.2 75.9Philippines 50 2947 47.5 81.5Singapore 30 223 49.6 87.0Thailand 57 662 29.7 80.5Vietnam 55 3849 98.1 85.0Other Southeast Asia 14 99 80.8 78.2China 50 2223 78.2 70.0Hong Kong 21 798 90.3 65.0Japan 70 657 22.0 82.5Korea 36 251 85.2 77.7Taiwan 10 157 94.5 100.0Afghanistan 28 153 97.2 64.0India 28 1172 95.5 70.5Sri Lanka 26 549 92.0 88.5Other South Asia 19 455 63.4 56.4Total 613 15760 73.9 78.3Source: Longitudinal Survey of Immigrants to Australia (LSIA)


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Spouse migration from Asia to Australia is predominantly female. Nearly four out of fivespouse migrants from Asia in recent years were women. The resulting sex ratio of about 27males per 100 female migrants was much lower than that of all spouse migrants to Australia,which was about 50 males per 100 females (Khoo, 2001). Spouse migration from Asiancountries is therefore comparatively much more dominated by women. The proportion of

female migrants was a bit lower for some South Asian countries and for Hong Kong, but itwas still about two-thirds.

The proportion married to partners from the same cultural background varied enormously bycountry of origin. Almost all spouse migrants from Vietnam were married to or migrating tomarry someone also born in Vietnam. The proportion in intracultural marriages was also morethan 90% for migrant spouses from Cambodia, Hong Kong, Taiwan and most South Asiancountries. However, about half of all spouse migrants from Indonesia, Singapore andPhilippines were in intercultural marriages and the proportion in intercultural marriages washighest for migrants from Japan, followed by those from Thailand.

Figure 16 shows the birthplace of the Australian resident partners of the migrants: for those inintracultural marriages, whether the partner was from the same country or same region oforigin; and for those in intercultural marriages, whether the partner was born in Australia orelsewhere (but not from Asia). Almost all the migrant spouses from Vietnam were married to

partners who were also born in Vietnam. There was also a high proportion marrying ormarried to partners from the same country among spouse migrants from Afghanistan, SriLanka, India, Korea and Cambodia.

Figure 16 Migrants in intra- and intermarriages:distribution by birthplace of spouses

0%10%20%30%40%50%60%70%80%90%

100%

V i e t n a

m

A f g h a n

i s t a n

S r i L a n k a I n d

i a K o

r e a

C a m b o d

i a

H o n g

K o n g

T o t a l

I n d o n e

s i a C h

i n a

M a l a y

s i a

P h i l i p p

i n e s

T a i w a

n

S i n g a p

o r e

T h a i l a n

d J a p

a n

Birthplace of migrant

Intra-marriage: Spouse's birthplace: same country Intra-marriage: Spouse's birthplace: same region

Intermarriage: Spouse's birthplace: Australia Intermarriage: Spouse's birthplace: other


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Migrants from Taiwan tended to marry people who were not born in Taiwan, but born in thesame region and therefore likely to be of the same cultural background. This was also the casefor a significant proportion of spouse migrants from China.

Migrants from Japan had the highest proportion marrying or married to Australian-bornspouses. Next were migrants from Thailand, followed by those from Singapore andPhilippines. About one in four migrants from Japan and Thailand were in intermarriages with

people who were not born in Australia (most were born in Europe or New Zealand).

Differences between Intramarried and Intermarried Migrants

There are some important differences between the migrants in intracultural marriages andthose in intercultural marriages in their demographic and socioeconomic characteristics.Female migrants in intracultural marriages were younger than those in intercultural marriages;however male migrants in intracultural marriages were slightly older than those inintercultural marriages (Table 4).

Spouse migrants were generally younger on average than other non-spouse migrants fromAsian countries. This was because about one-third of them were migrating as prospectivespouses who tended to be young women or men of marriage age, and also because other non-spouse Asian migrants included skilled and business migrants who tended to be older andelderly parents who had been sponsored by their children.

A much lower proportion of intramarried migrants said they could speak English wellcompared to intermarried migrants. This was not surprising as intramarried migrants werelikely to speak their native language within the family. For the large proportion not able tospeak English well, this would affect their ability to communicate with the wider community.It would also affect their participation in the work force, for which English proficiency isimportant. The proportion speaking good English was higher among intermarried migrantsthan other Asian non-spouse migrants. This was true of both men and women. This wouldsuggest that intermarried migrants would be the best placed among the three groups in termsof social and economic integration.


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Table 4 Background Characteristics of Migrant Spouses byType of Marriage and Sex

Characteristics of Males Femalesmigrant spouse Intra-

marriageInter-

marriageOther Intra-

marriageInter-

marriageOther

Mean age (years): 32.7 31.4* 36.2 # 28.2 31.7* 36.7 #

% spoke English well: 51.5 88.6* 65.9 # 33.0 77.9* 59.7 #

Education

% with tertiary degrees: 34.9 20.5 42.7 # 24.7 31.7 44.6 #

% with technical quals: 30.9 36.6 26.0 # 13.8 23.1 11.6 #

% with no qualifications: 34.2 42.9 31.4 # 61.5 45.1 43.8 #

% employed before 72.8 85.0 78.4#

62.3 66.7 60.0migration:

% with relatives in Aust. 72.3 71.8 48.8 # 55.6 71.2* 57.6 #

% visited Australia before

migration 42.8 83.5* 32.4 # 19.4 48.3* 40.6 #

% who said idea to migrate was:

- their own: 24.5 28.2 52.4 # 17.0 31.1* 51.5 # -sponsoring spouse's: 39.9 25.4 5.2 50.4 23.4 4.5- joint: 32.7 46.4 23.4 27.5 43.4 13.0- others': 2.8 0 19.0 5.2 2.1 31.1

Number of respondents 110 36 920 285 181 457 Number of migrants 2876 544 11786 8746 3566 5943Source: LSIA

* Difference between intramarriage and intermarriage groups significant at p=0.05

# Diference between the three groups significant at p=0.05


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Although intramarried and intermarried migrants differed somewhat in their level ofeducation, the difference was not statistically significant. A higher proportion of intermarriedwomen than intramarried women had post-school qualifications, but the opposite pattern wasobserved for men. Spouse migrants—both men and women—were not as well qualified asother non-spouse migrants from Asia.

Intermarried men were the most likely to be employed before migration. But there was notmuch difference in the proportion employed before migration among the three groups offemale migrants.

A high proportion of male migrants had relatives in Australia and there was no difference between those in intracultural and intercultural marriages. However, among female migrants,those in intramarriages were less likely than those in intermarriages to have relatives inAustralia. Most intermarried male migrants had also visited Australia before migration, andthe male spouse migrants were in general much more likely to have visited Australia beforemigration than the female migrants. This would have been helpful to them when theymigrated as it would not be the first time that they had been in Australia. In contrast, most ofthe women in intracultural marriages had not been to Australia before migrating.

Intramarried spouses also had a lesser role in the decision making process about theirmigration compared with intermarried spouses, particularly among the women. Half of allwomen in intracultural marriages reported that it was their spouse’s idea that they migratedcompared with less than one-quarter of women in intercultural marriages. Less than one infive intramarried women said it was their own idea to migrate. Less than 30% said it was a

joint decision between them and their partners compared to over 40% of intermarried women.

These observations would suggest that women in intracultural marriages were likely to bemore disadvantaged in their settlement and adjustment process compared with women in

intermarriages and male spouse migrants generally. They were likely to be more isolated fromthe wider community (because of their inability to speak good English) and less likely to haveany relatives in Australia. For most of them, their migration was the also first time that theyhad set foot in Australia and the idea of their migration had been not been theirs, but theirhusband’s.

Settlement Outcomes

The settlement experiences of the spouse migrants were examined at about 6 months and 18months after arrival. Five dimensions of settlement were examined, encompassing both the

personal and social spheres. Those relating to the personal sphere were family situation,economic and physical wellbeing, while those relating to the social sphere were participationin employment or education. Intramarried migrants might have less to adjust to in the

personal sphere because their spouse was from the same cultural background, but might take


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longer to adjust to in the social sphere since they were less likely to be proficient in English.In contrast, intermarried migrants might have more adjustment to make in the personal spheresince their spouse was from a different cultural background, but their adjustment in the socialsphere might be facilitated by their better English and greater familiarity with Australia due to

previous visits and having relatives here.

Family Situation

The first indicator of the migrants’ family situation to be examined was their marital status atthe second interview, 15 to 18 months after arrival. The aim was to determine what

percentage of the marriages had broken down. The proportion still married was 98% forwomen but lower for men (Table 5). Among the men, the proportion still married was alsolower among intramarried migrants than intermarried migrants, but the difference was toosmall to

asians on the move

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