Asians on the Move: Spouses, Dependants and Households

Special Collection of Papers by Chotib

Siew-Ean Khoo Salut Muhidin

Zhou Hao S.K. Singh

ASIAN METACENTRE RESEARCH PAPER SERIES

no. 8

ASIAN METACENTRE FOR POPULATION AND SUSTAINABLE DEVELOPMENT ANALYSIS

HEADQUARTERS AT ASIA RESEARCH INSTITUTE NATIONAL UNIVERSITY of SINGAPORE

Asians on the Move: Spouses, Dependants and Households

Chotib currently works in the Demographic Institute, Faculty of Economics at the University of Indonesia as a Researcher. He also teaches in the Masters Program for the Study of Population and Human Resources Economics and the Masters Program for Geography, at the same university. He graduated from the Department of Geography, Faculty of Mathematics and Natural Sciences in 1991, and received his masters degree from the Program for the Study of Population and Human Resources Economics in 1998 from the University of Indonesia. His research interests are in migration and urbanization, urban and regional planning, population projection, multiregional demography, labour, population and sustainable development.

Siew-Ean Khoo is Executive Director of the Australian Centre for 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 the ANU. Her research and publications have focused on family formation issues, ethnic demography and immigrant settlement, she has a Doctor of Science in Population Sciences from Harvard University.

Salahudin (Salut) Muhidin is a researcher from the Demographic Institute, University of Indonesia. Currently, he is at IIASA (International Institute for Applied Systems Analysis), Austria as a postdoctoral fellow. Muhidin holds a Ph.D. in demography from the Faculty of Spatial Science, University of Groningen, the Netherlands. His research areas are formal demography, multiregional/multistate analysis, population projections, and migration. His most recent publication is a book, based on his Ph.D. research, entitled “The Population of Indonesia. Regional demographic scenarios using a multiregional method and multiple data sources.”

ChotibSiew-Ean Khoo

Salut MuhidinZhou Hao

S.K. Singh

Zhou Hao, now is a post-doctor of Department of Sociology, Peking University. He holds a B.A. in Demography from Hangzhou University (1997), and a PhD in Demography from People’s University of China (2000). His research interests include migration, urbanization, fertility analysis, population development, population aging in China. Recently, most of Zhou’s research is connected to issues of migrant children, and the relationship between migration and family structure. He also teaches statistics, migration, and demographic methods. S. K. Singh is a lecturer in the Department of Public Health and Mortality Studies at the International Institute for Population Sciences (IIPS). He holds a Ph.D in Statistics/Demography from Banaras Hindu University in 1990. S. K. Singh has extensive research experience and his areas of research include rural-urban migration and return migration, assessment of women’s status, fertility, family planning and health, and infant and child mortality, and stable population theory. He is recently researching on knowledge about HIV/AIDS and risk behaviour among migrants in some selected developments projects. He has also published a number of book chapters and journal articles.

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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 1995 Intercensal Population Survey Chotib Chapter 2 26 Marrying and Migrating to Australia: Asian Spouses in Intra-and Inter-Cultural Marriages Siew-Ean Khoo Chapter 3 46 Migrated Household in Indonesia: An Exploration of the Intercensal Survey Data Salut Muhidin

Chapter 4 66 Migration and Household Characteristics: Evidence from China Zhou Hao Chapter 5 84 Some Probability Models for Estimating the Propensity of Dependants’ Migration in India and Their Applications S.K. Singh

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List of Tables

Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9 Table 10 Table 11 Table 12 Table 13 Table 14 Table 15 Table 16 Table 17 Table 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 Birth Background Characteristics of Migrant Spouses by Type of Marriage and Sex Settlement Outcomes at 6 Month (W1) and 18 Months (W2) after Arrival Maximum Likelihood Estimates of the Effects of Migrant Characteristics on Employment Status at 6 Months (W1) and 18 Months (W2) after Arrival Operational Definitions of Variables Considered for the Analysis of Migrated Households, Indonesia, 1995 Bivariate Analysis of the Selected Characteristics for Migration Status of Households, Indonesia, 1995 Logistic Regression Analysis for being a Migrated Household, Indonesia, 1990-1995 Independent Variables Used in Analysis Distribution of Household with Migrant in Urban and Rural Area Average Size of Household Average Number of Adult and Children in a Household Average Number of Migrant in a Household Logistic Regression Result (Total Population) Logistic Regression Result (Male) Logistic Regression Result (Female) Distribution of Households According to Number of Migrants (Males aged fifteen years and above in different types of villages) Distribution of Households According to the Number of Migrants (Males aged fifteen years and above) in Different Caste Group Distribution of Households According to Number of Male Migrants (aged fifteen years and above) in which the Number of Male Migrants is Less Than or Equal to the Number of Dependant Migrants in Three Types of Villages, viz, Semi-Urban, Remote and Growth-Centre Distribution of Households According to Number of Dependant Migrants from a Household with at Least One Male Migrant Aged Fifteen and Above, in Three Types of Villages, viz, Semi-Urban, Remote and Growth-Centre Joint and Marginal Probabilities of i Male Migrants Aged Fifteen Years and Above and n Dependants to be Migrated from a Household in Three Types of Villages

13

18

31

34

37 41

56

59

63

71 72 73 74 75 77 78 79 89

90

94

95

96

iii

List of Figures

Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18

The Full Model of Migration Schedule Age Pattern of Out-Migration from Jakarta Age Pattern of In-Migration into Jakarta Age Pattern of Male Out-Migration from Jakarta Age Pattern of Female Out Migration from Jakarta Age Pattern of Out-Migration from Jakarta into Urban Areas Age Pattern of Out-Migration from Jakarta into Rural Areas Age Pattern of Jakarta Born Out-Migration from Jakarta Age Pattern of Non-Jakarta Born Out-Migration from Jakarta Age Pattern of Male In-Migration into Jakarta Age Pattern of Female In-Migration into Jakarta Age Pattern of In-Migration into Jakarta from Urban Areas Age Pattern of In-Migration into Jakarta from Rural Area Age Pattern of Jakarta Born In-Migration into Jakarta Age Patten of Non-Jakarta Born In-Migration into Jakarta Migrants in Intra- and Intermarriages: Distribution by Birthplace of Spouses Proportion of Migrants by Migration Triggers, Indonesia 1980-1985 Proportion of Migrants by Migration Triggers, Indonesia 1990-1995

8 22 22 22 22 22 22 23 23 23 23 23 23 24 24 32 49 50

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Chapter 1

Age Pattern of Migration from and into DKI Jakarta, Indonesia: An Analysis of the 1995 Intercensal Population Survey

Chotib

Demographic Institute Faculty of Economics

University of Indonesia

Abstract

Age-specific fertility and mortality rates have been shown to exhibit remarkably persistent regularities. Consequently, demographers have found it possible to summarize and codify such regularities by means of mathematical expression. Although the development of the models of fertility and mortality schedules has received considerable attention in demographic studies, 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 schedules can be readily extended to deal with the migration schedule.

This research examines age-sex specific migration patterns into and out of DKI Jakarta based on SUPAS 1995 (1995 Intercensal Population Survey). The estimation is carried out in line with 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 the out-migration from the rest of Indonesia to DKI Jakarta. Another feature of this research is the existence of control variables (gender, urban-rural characteristics, and place of birth) in the estimation of the migration pattern.

It demonstrates that out-migration from DKI Jakarta (to the rest of Indonesia) is more “child dependent”, 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 is higher than the intensity of male migrants; the intensity of urban to urban migration is higher than the intensity of urban to rural or rural to urban migration; and the propensity to move out of 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 is almost seven times as high for migrants who were born in DKI Jakarta than for those born in the 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 Jakarta Capital City is very high. In the period of 1975–1980, the number of recent migrants to DKI Jakarta was 766,363, mostly from Central Java (34.3%) and West Java (27.6%). The number of recent migrants from DKI Jakarta was 382,326, mostly to West Java (64.6%) and Central Java (9.8%) as their places of destination. Hence, DKI Jakarta had the biggest net positive number of recent migrants, with about 384,037 people.

However, since 1990, the pattern has been reversed. In the period of 1985–1990, the total number of migrants to DKI Jakarta was 833,029, mostly from Central Java (40.5%) and West Java (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 receiving destination to a migrant supplier.

This change is not without the role played by West Java Province as a place of destination for migrants 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 find a solution for the population problems of DKI Jakarta. This concept is no other than a policy on spatial distribution of the city planning of Jakarta City, which is trying to disperse new activities as well as some of those already in operation within the new developing areas. It was expected that these new areas would be able to attract the population of Jakarta to live there. New jobs would be created along with regional transportation network and infrastructures in designated centers of settlement (Suselo, 1977).

The expectation became a reality as shown by the change of migrant destination between Jakarta and West Java in 1980 to 1990. The 1995 Intercensal Population Survey data illustrates 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 DKI Jakarta 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 rate of 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% to East Java (Central Bureau of Statistics, 1997). Most of those whose destination was West Java were settling in the peripheries of Metropolitan Jakarta known as JABOTABEK. They were drawn to these areas by the development of new settlements and availability of employment

J

4

opportunities especially in the industrial sectors. At the same time, those in-migrants to DKI Jakarta 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 both the generating regions and the destination regions. The analysis of the 1990 Population Census by Firman (1994) shows an almost similar pattern.

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

Data and Method

This study uses 1995 Intercensal Population Survey (SUPAS, 1995). Migration status from SUPAS 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 years earlier. ‘Province’ is therefore the unit of analysis. Hence three types of migration are identified as follows:

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

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

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

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

The dependent variable is migration rate and the main independent variable is the age of respondent. This rate is obtained from the proportion of the population who were stated as migrants for each year of age of respondents. Therefore, the variables selected from the SUPAS 1995 raw data were current province of residence, province five years earlier, and age of respondents. The age-specific pattern was then controlled by migrant characteristics such as gender, province place of birth, and urban/rural area of origin/destination at current residence 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 by the schedule based on the least square method. This method is formulated as follows:

(2)

where:

(3)

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

The procedure mentioned above is intended to obtain θ = (a1, a2, a3, 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”; di is direct of difference (negative or positive value); and i is number of 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 a number of sub-national populations. Populations who were born in a region (sub-national region) has the risk to move to other places. Rogers (1995) has extended the theory of

∑= xASMRGMR

[ ]∑ −=2

),(ˆ),()( θθθ xMxMQ

{ } { } { } cx

exax

exaxxaxM +−−

−−−+−−

−−−+−=)3(3)3(3exp3

)2(2)2(2exp2exp1)(µλ

µαµλ

µαα

iiii dβθθ +=+1

εθθ p−+1i

6

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

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

In this system, the migration outflows from the rest of the country define the migration inflow of a region in the country. The uniregional analysis of the population changes in all of regions focuses on the changes in the outflows and inflows in each region, one at a time. On the other hand, the multiregional perspective regards all of regions as a system of inter-regional interacting populations, with a pattern of outflows. Moreover, the multiregional approach employs rates of flow that refer to the appropriate at-risk populations; the uniregional approach 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. The measurement e0 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, e65 is the life-expectancy rate that would be lived out by people after they reach the age of 65.

The measurement of fertility rate is principally about the expected number of persons (that were born) or number of births. Measurements such as Total Fertility Rate (TFR) or Gross Reproduction Rate (GRR) show the number of births by women during their reproductive age. In the GRR, the number of baby girls is specially identified. Fertility is a repetitive event in 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 the interaction between mortality and fertility will produce a measurement called NRR (Net Reproduction Rate) a continuation of GRR. It differs from GRR in that the GRR starts with female population age 15 (the beginning of reproductive age), and that they have to remain alive 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 their reproductive 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 be calculated 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 very rough 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 Specific Migration Rate), indicating the number of migrants of a certain age specific among people who 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 duration of time. For example, GMR=14 in 1993 denotes that a person will out-migrate about 14 times during his/her life time (that is until his/her mobile age) provided he/she is not “permitted” to die 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 (Net Migra-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) generally shows a high rate of migration, while those in the middle age group has the lowest rate of migration. 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, the destination of migration tends towards regions with a cool climate or cities that provide sufficient 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 a retirement peak as full model of a variety of available schedule models. It also shows a broken graphic indicating a decomposition of a continuous graphic. There are four kinds of broken graphics identified in this figure. They are: 1. Pre-labour force, shown by an exponential curve with decreasing rate of a1.

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

3. Post-labour force, a curve of a bell-shape showing a mean age rate of µ3, an increasing rate 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:

{ } { } { } cexaexaxaxM xx +−−−+−−−+−= −−−− )(333

)(22211

3322 )(exp)(expexp)( µλµλ µαµαα (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 a1, a2, a3, and c. A change in GMR will modify 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. The change of profile will modify these seven parameters, but they will not necessarily change the other four parameters.

Some interesting phenomena indicated in Figure 1 include the three special points in the migration 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. At this level M(x) becomes the highest point compared to other points even though it is outside 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=xh – xl which is an age difference between the lowest point and the 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 parental migration. 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 two age 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 = a1/a2 is less than or equal to 1/5. If the δ12 is above 1/5 up till 2/5, the schedule is considered normal. When δ12 is above 2/5, the schedule is called ‘child dependant’.

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

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

Based on the migration pattern of pre-labour force curve, Rogers (1984) classified the schedules 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 curve created at the post-labour force is an exponential curve upwards. Hence the mathematical equivalence is as the following:

{ } { } ( ) cxaexaxaxM x ++−−−+−= −−33

)(22211 exp)(expexp)( 22 αµαα µλ (8)

3. Simple model. This model has no age pattern for the post-labour force. Hence the

mathematical equivalence is as follows:

{ } { } cexaxaxM x +−−−+−= −− )(22211

22)(expexp)( µλµαα (9)

The model is applied to DKI Jakarta and the rest of Indonesia. However, to focus on DKI Jakarta, 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 of DKI 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 follows the pattern available during 1990–1995.

A comparison of the parameter rates of a1, a2, a3 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 a1<a2>a3, where a1 is the migration level of pre-labour force age group; a2 is the migration level of labour-force age group, and a3 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 of migration (GMR). This pattern refers to whether the curve is symmetrical or asymmetrical. To measure 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 is definitely 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 has reached 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 of the 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 even quicker than the increase as shown by the value of σ3 of 0.15 (less than 1). The peak of migration will occur when these groups reach the age of 58 with a rather slightly sloping increase and then decreasing more quickly until it slows down again at the end of their mobile age. Therefore the mean age of the migrants of this group is 77 years. It is at the far right of the 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 children migrants 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 when they 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 By Place of Birth

Parameter All of Migrants

Male Female Urban Rural Jakarta Non-Jakarta

GMR (data) 5.995650 5.743720 6.170010 4.110370 2.076909 3.365740 10.45997 GMR (model) 6.004874 5.749329 6.168259 4.109458 2.091313 3.366124 10.46195 a1 0.1365 0.1600 0.1710 0.0925 0.0556 0.1087 0.949378 α1 0.0518 0.0491 0.0352 0.0321 0.1134 0.0591 0.155249 a2 0.1371 0.1820 0.0922 0.0855 0.0834 0.0944 0.407319 α2 0.0330 0.0374 0.0056 0.0275 0.0947 0.0747 0.056143 µ2 19.4842 21.0963 17.4714 19.4645 21.6836 22.9151 16.93153 λ2 0.3553 0.2815 0.8461 0.3271 0.2542 0.2330 0.214636 a3 0.0001 0.0001 0.0001 0.0000 0.0000 0.0001 0.000744 α3 0.6769 0.6849 9.0038 0.6848 0.6851 0.6850 0.263094 µ3 77.1983 74.3823 60.2773 76.4264 77.1502 71.9041 100.1577 λ3 0.1017 0.1018 1.1793 0.1020 0.1022 0.1019 0.046156 c 0.0017 -0.0144 -0.0380 -0.0108 0.0109 0.0064 -0.00153 Mean age (data) 36.373784 33.929516 38.512938 36.150872 36.384743 32.879875 30.48024 Mean age (model) 36.485950 33.965714 38.536428 36.142740 36.789072 32.921769 30.50228 σ2 10.767143 7.522860 51.448188 11.915267 2.686127 3.120246 3.823023 σ3 0.150273 0.148568 0.130980 0.148932 0.149241 0.148803 0.175436 δ12 0.994978 0.879189 1.855902 1.082001 0.666488 1.151374 2.330797 δ32 0.000607 0.000279 0.001419 0.000560 0.000405 0.000842 0.001826 xl 15.379876 15.938934 15.652245 14.863987 16.251079 16.994732 12.69041 xh 24.854771 26.910346 21.115083 25.747583 24.218672 26.392229 21.51552 xr 57.963349 54.879523 58.553212 57.083785 61.113511 52.873462 58.91291 X 9.474895 10.971412 5.462838 10.883596 7.967593 9.397496 8.825109 A 30.822764 32.868129 28.849299 33.179814 27.570810 20.893222 39.970239 B 0.073000 0.086982 0.068168 0.044669 0.033285 0.026934 0.074869

14

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, which is 6.2 for women and 5.7 for men. The difference also occurs in the mean age of the labour force group. The female migrants are mostly younger than the male migrants (17 and 21 years old respectively). Similarly with the post-labour force group where the mean age of female migrants is 60 years, while male migrants’ mean age is 74 years. The lower mean age of female migrants is thought to be the result of being married (as wife), and they are mostly younger than their husband (the male migrants). In general the reason for women to migrate out of DKI Jakarta is the family.

However, when looking into the rate in general, the mean age of female migrants tend to be older than that of the male migrants. The mean age of female migrants is 38.5 years, while male migrants’ mean age is 34 years. The high mean age of female migrants is very much affected 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 lower than the peak of the labour-force group. The great numbers of old women who migrate out of DKI 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 of migration rate towards the peak age of the labour force is higher in female migrants than in male migrants. The length of labour force age (X) for females is also 5 years shorter, starting from age 16 to age 21 years, while the X rate of male migrants is 11 years longer, between 16 and 27 years.

Controlled by Urban-Rural Residence

Urban or rural areas are used to classify the place of destination of the migrants from DKI Jakarta to outside DKI Jakarta. Quantitatively the GMR to urban areas is 4 while the GMR to rural areas is 2. The higher intensity of migrants to urban areas is the result of the emergence of 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. The increase of migration rate of this group is so steep as indicated by σ2=12 with a mean younger age as well (µ2=19 years), while the rate of rural areas is σ2=3 and µ2=12 years. The range of labour force curve (X) to urban areas is also longer than the one to rural areas. X means the difference between age when migration rate is highest and age when migration rate is lowest

15

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

The dominance of labour force age group migrating to urban areas is also indicated by the higher curve peak than the one to rural areas. The peak of migration to urban areas is almost 0.1, while the one to the rural areas is a little above 0.05. Moreover, the start of increase of the migrants to urban areas is at a younger age (15 years) than to rural areas (16 years) although the peak age curve of the labour force group to urban areas is older (26 years) than to rural areas (24 years). If young migrants are a little dominant in the urban areas, the old migrants are very dominant in the rural areas. The mean age of post labour force group in rural areas is 77 years, one year older than in the urban areas. Similarly, the peak curve of the post-labour force group of migrating out to rural areas is 61 years, and the peak for migrating out to urban areas is younger, 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 of migration behavior (Ledent and Termote, 1992). The dimension is reflected in the multiregional analysis of population projection, which simultaneously integrates the three demographic components (fertility, mortality, and migration). With the inclusion of place of birth and migration behavior, the life expectancy rate is no more analyzed uniregionally. The death 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 a mother, but where a mother was giving birth which essentially is related to the life expectancy rate 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 the MGR for non-native Jakartanese migrants (born outside Jakarta) is 1.36; almost double the rate of native migrants (born in Jakarta) of 0.75.

Meanwhile Kao and Hayase (1997) reported that the same case has also happened in Zimbabwe during the period of 1982–1992. They discovered empirically that migrants born in 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 much lower for migrants born in urban areas.

16

The analysis with the 1995 SUPAS indicates that GMR out of DKI Jakarta for migrants born outside 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 in Jakarta 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 “labour dominant”. In other words the two groups of migrants are more dominated by the pre-labour force 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 have similar 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 migration intensity as indicated by GMR of 0.2. This small GMR does not denote that the absolute number of migration is also small. The rate is small because the denominator is the rest of Indonesia 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 includes those 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 also shows that the curve of the labour force group is symmetrical with σ2=1.9. This indicates that the increase of migration towards the peak age of the labour force curve is almost the same as the decrease of the migration after the peak.

Another characteristic of out-migration is the relatively small migration level of pre-labour force age group. Hence the δ12 value—which indicates the ratio between a1 and a2—is so small that it is close to 0.07. The schedule of in-migration is more labour dominant than child dependant. This is natural since labour force age group who do not always bring their children or family along with them mostly dominates migration into DKI Jakarta. The migrants were more economically motivated (looking for jobs) and therefore they have the propensity to leave their family behind. Some of them will occasionally return to their native places temporarily, and some others do not, but they send remittances.

17

Empirically the mean age of the migrants is 28 years. This age is much younger than the mean age of out-migrants, but the mean age of the labour force (µ2) is relatively the same, at 19 years. However, the beginning of the labour force curve for in-migration is earlier than the out-migration. The curve of out-migration started at the age of 15 with the peak at age 25, but the 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 the out-migration (about 77 years). However, the curve for the post-labour force group does not have 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 and male 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-labour force age. Yet, seen from the total population point of view, the intensity of the female migrants is relatively higher than that of male migrants. The GMR of the female migrants is 0.2, higher than 0.18 for the male migrants. See Figure 11. The higher intensity in the total female population than in the male population is because of the relatively large proportion of females in the labour force group.

In general the mean age of the female migrants is two years younger than the age of male migrants. 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 22 years. Nevertheless, the mean age of female migrants for the post-labour force group is higher than that of male migrants. In this group the mean age is 81 years for female migrants and 76 years 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 By Place of Birth

Parameter All of Migrants

Male Female Urban Rural Jakarta Non-Jakarta

GMR (data) 0.207090 0.182514 0.212666 0.248387 0.174143 1.334288 0.188098 GMR (model) 0.207085 0.182465 0.212665 0.248389 0.174143 1.334293 0.188101 a1 0.001597 0.001541 0.001180 0.003346 0.003818 0.346906 0.001325 α1 0.075453 0.059860 0.062717 0.078598 0.251050 0.023749 0.075313 a2 0.022252 0.022074 0.025808 0.018012 0.026450 0.084894 0.021899 α2 0.128929 0.146481 0.143464 0.094118 0.160524 -0.022569 0.131966 µ2 18.668815 22.193010 17.071280 18.344844 19.714595 14.875193 18.737828 λ2 0.250957 0.202187 0.319129 0.286646 0.21144 0.338004 0.250163 a3 0.000000 0.000000 -0.000002 -0.000000 0.00000 -0.035152 0.000000 α3 0.687575 0.692203 0.670126 0.681632 0.522520 -0.046850 0.689071 µ3 77.648759 77.5587982 81.532215 77.553257 82.387899 47.038439 77.607860 λ3 0.102400 0.101734 0.109193 0.103025 0.067118 0.773279 0.102178 c 0.000346 0.000263 0.000519 0.000602 0.0001175 -0.255559 0.000336 Mean age (data) 28.046984 29.640830 27.162742 30.699060 26.791715 26.089526 28.281014 Mean age (model) 28.187347 29.793561 27.389420 30.816988 26.668253 26.089443 28.439477 σ2 1.946468 1.380292 2.224444 3.045619 1.317192 -14.976263 1.895654 σ3 0.148930 0.146971 0.162944 0.151145 0.128450 -16.505467 0.148283 δ12 0.071769 0.069827 0.045725 0.185751 0.144330 4.086358 0.060515 δ32 0.000014 0.000037 -0.000078 -0.000015 0.000006 -0.414075 0.000012 xl 10.565128 11.845485 10.404852 11.919979 10.009289 12.315465 10.486452 xh 21.244200 23.698802 19.535518 22.043569 21.003173 21.898822 21.228689 xr 56.029463 58.049494 80.006974 65.252028 50.313562 54.664874 55.932976 X 10.679072 11.853318 9.130666 10.123590 10.993885 9.583358 10.742237 A 36.909934 36.861828 35.647470 35.247599 34.595308 * 37.741329 B 0.009025 0.008052 0.011209 0.008214 0.009701 0.030475 0.008862 Note: *) No logical value

19

The analysis also finds a steeper increase of migration rate towards the peak age of the labour force. The σ2 parameter for female migrants has the value of 2, and the peak migration rate is also high, almost 0.013. However, the value of σ2 for male migrants is 1 with a lower peak migration 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 the curve between age 10 and 19 years.

Controlled by Urban-Rural Characteristics

There is no difference between curve profiles (patterns) of migration from rural areas and urban 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 than those from rural areas (with GMR=0.17). This result may seem to be in conflict with the finding, 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 by GMR is an age-standardized measurement. Because rural areas have a larger young age group, both in percentage and absolute number, the percentage of migrants entering to DKI Jakarta is dominated by those from rural areas. However, the seemingly dominant rural migrants disappear as soon as we control the age composition by using GMR as the measurement of migration.

A variation is also found in the migrants’ mean age. In general, the migrants from urban areas are 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) than migrants from rural areas (83 years). There is no much difference in the mean age of migrants among 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 DKI Jakarta have a greater propensity to return to DKI Jakarta—7 times higher than those born outside Jakarta. The GMR of those born in Jakarta is 1.3 times, and the GMT of those born outside Jakarta is only 0.19. A similar case was also found in the study by Ledent and Termote (1992), which shows that the in-migration intensity to DKI Jakarta of migrants born in 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”.

20

If the life expectancy rate of someone is decomposed into specific regions, it is apparent that there 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 a1 value—indicating the migration level of pre-labour force group—which is higher for those born in Jakarta (0.35) than for those born outside Jakarta (0.001). The very high migration level of the pre-labour force group 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 more child-dependent. On the contrary, migrants born in Jakarta have higher migration level for the labour-force group than the pre-labour force group and therefore the migration profile is more labour dominant.

This difference of characteristics shows that migrants born outside Jakarta are more economically motivated and mostly dominated by the labour-force age group. Whereas migrants born in Jakarta are more motivated by non-economic activities. According to the data 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 is compared to migrants born in Jakarta, more than half of whom (52.73%) said they came to Jakarta to join the family.

There is a great difference in the curve profiles of the two groups of labour-force migrants to Jakarta. 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 these migrants 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 in general, with a cone-shaped curve with a higher peak. This shows that the labour-force age group 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 years younger than migrants born outside Jakarta. A similar result is found with the labour-force curve. The mean age of migrants born in Jakarta has µ2 value of 14 years, while the µ2 of those born outside Jakarta is 19 years. Another similar result is observed in the post-labour force 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.

21

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 from urban to rural areas (Skeldon, 1990). Carr (1997) also mentions that in the developing countries, patterns of internal migration are associated with the main urban metropolitan regions. Whereas in the advanced countries, the form of migration is often of the form of movement 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 from urban areas. The finding indicates that Indonesia has reached in the last stage of mobility transition, though it has not found urban to rural migration as observed in developed countries.

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 an area of destination, where people can come to improve their lives and standards of living. On the other hand, people leave DKI Jakarta because of non-economic reasons such as joining the family, housing problems, and educational opportunities.

22

Figure 2 Age Pattern of Out-Migration from Jakarta

Figure 3 Age Pattern of In-Migration into Jakarta

Figure 4 Age Pattern of Male Out-Migration from Jakarta

Figure 5 Age Pattern of Female Out Migration from Jakarta

0

0.05

0.1

0.15

0.2

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

Data Full Model

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Data Full Model

AGE

ASM

R

Figure 6 Age Pattern of

Out-Migration from Jakarta into Urban Areas

Figure 7 Age Pattern of Out-Migration from Jakarta

into Rural Areas

0

0.05

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AGE

ASM

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23

Figure 8 Age Pattern of Jakarta

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

Born Out-Migration from Jakarta

0

0.05

0.1

0.15

0.2

0.25

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AGE

ASM

R

Figure 10 Age Pattern of Male

In-Migration into Jakarta Figure 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

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0.008

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Data Full Model

AGE

ASM

R

Figure 12 Age Pattern of

In-Migration into Jakarta from Urban Areas

Figure13 Age Pattern of In-Migration into Jakarta from

Rural Areas

0

0.002

0.004

0.006

0.008

0.01

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0.014

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0.008

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0.014

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ASM

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24

Figure 14 Age Pattern of Jakarta Born In-Migration into Jakarta

Figure 15 Age Pattern of Non-Jakarta Born 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

ASM

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0

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ASM

R

25

References Ananta, A. (1990) Parameter Penduduk Stabil (Stable Population Parameter), Jakarta: Demographic Institute Faculty 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 Population Survey 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 Migration and 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 and Sons.

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 Sciences The Australian National University, Australia

Abstract

This paper uses longitudinal survey data to examine the initial settlement experience of a cohort of spouse and prospective spouse immigrants from Asian countries who are married to or migrating to marry Australian residents who are of the same or different cultural background. It investigates their family circumstances and socioeconomic status during their first two years of residence in Australia, and tests the hypothesis that migrant spouses in intercultural marriages adjust more quickly to Australian society than migrant spouses in intracultural marriages. It also compares the migrant spouses with other non-spouse migrants from 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 ways in their socioeconomic and migration background that in turn resulted in differences in their adjustment and settlement experiences after migration. It also confirms the hypothesis that intermarried migrant spouses adjust more quickly to Australian society as indicated by their better English language skills and greater participation in employment than intramarried migrant spouses.

27

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 been about 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, about 16,000 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 of immigrant spouses is not limited to citizens. There is also no limit to the number of spouse immigrant visas that can be issued each year in Australia’s immigration program because of its commitment to the objective of family reunion, although quotas have been introduced on the 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 a spouse.

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 prospective spouse. Some of the migrating spouses are married to or marrying partners of the same cultural background who have migrated earlier. Others are married to or marrying partners who are from a different cultural background, either native-born Australians or immigrants from another part of the world.

Marriage and migration are major life cycle events. In most cases, each represents a change in life style and in the case of migration, it is always associated with a change in residential location. Each requires the individuals involved to adjust to these changes. For the men and women who are combining marriage and immigration, they have to make two types of adjustment 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 enough about the dual adjustment processes faced by immigrant spouses to produce an information video and accompanying booklet called Marrying and Migrating to Australia to provide pre-migration information to migrating spouses. This video followed an earlier one aimed at migrant spouses from the Philippines, which has been a major source of spouse migration since the late 1970s. The videos address adjustment issues faced by spouse and prospective spouse immigrants. These issues can be quite different depending on whether the migrant spouse 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 terms of adjusting to a new country as their spouse will have been resident in Australia for some time. This will also be true of immigrants who are migrating to marry someone of the same cultural 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 the family 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 wider community 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 a cohort of spouse and prospective spouse immigrants from Asian countries who are married to or migrating to marry Australian residents who are of the same or different cultural background. It investigates their family circumstances and socioeconomic status during their first two years of residence in Australia and tests the hypothesis that migrant spouses in intercultural marriages adjust more quickly to Australian society than migrant spouses in intracultural marriages. It also compares migrant spouses with other Asian migrants to see whether their settlement experience differs from that of other Asian migrants and requires special attention or assistance.

Spouse migration to Australia has been the subject of several studies. Many of these had focussed on the ‘mail order bride’ migration from the Philippines, which began in the late 1970s and was characterized by the migration of Filipino women who were married to Australian-born or European-born men. The studies had arisen partly from concerns about the incidence of marriage breakdown involving some of these women and there was some debate about 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’ of foreign-born spouses—the sponsoring of a succession of marriage partners after a breakdown in the relationship with the previous migrant partner(s)—which led the Department of Immigration 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 led to an examination of the different contexts of spouse migration. Birrell (1995) suggested that there 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. This type of spouse migration from Asian countries is usually associated with intermarriage. The second 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 been sources of refugee migration such as Vietnam, Cambodia and Afghanistan has been of this type. The third type is that of marriage migration where previous immigrants return to the home 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 Australians returning 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 ethnic identity. Khoo (1997; 2001) recently examined survey data on spouse migrants and their sponsors 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 spouse migration, with only a small proportion being family reunion migration.

While these studies have focussed on the characteristics of spouse migration, it is not known how these migrants adjust to new marriage partners and a new country at the same time. A study by Penny and Khoo (1996) on immigrants intermarried with Australians suggested that the outcomes relating to personal identity and integration into Australian society varied enormously across individuals. Since their study focussed on intermarriage, it did not examine migrants in intracultural marriages. This paper compares spouse migrants in intracultural and intercultural 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 of Immigrants to Australia (LSIA), conducted by the Department of Immigration and Multicultural 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 August 1995 and who were the principal applicants for permanent resident visas, stratified by visa category, State of residence and country of origin. The sample was first interviewed within 3 to 6 months of their arrival in Australia. Interviews were conducted face-to-face by trained interviewers, with about one-third of the interviews involving bilingual interpreters. The respondents were followed up one year later and re-interviewed. The attrition rate between the two interviews was about 15%.

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

Included in the survey were 613 Asian-born men and women who were migrants in the spouse or prospective spouse visa categories1. Data were missing for some variables of interest for one respondent. The remaining 612 respondents represented 15,732 Asian-born immigrants who were in the spouse or prospective spouse visa categories who arrived during the two-year sampling period, September 1993 to August 1995. Of the 612 Asian migrant spouses, 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 migrants such as skilled migrants, refugees and other humanitarian migrants, and other family reunion migrants such as parents and dependant children.

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

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

Data were also available from the survey about the Australian resident spouses of these migrants: whether they were born in Australia or overseas, and if overseas-born, their country of birth and when they migrated to Australia. From information about the country of birth, it was 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 intracultural marriage if the sponsoring spouse was born in the same country or the same region. For example, 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 sponsoring spouse was born in Australia or an overseas country that was not within the Asian region (for example, Europe or North America or New Zealand), then the marriage was assumed to be intercultural. For example, some Philippines-born women were married to men born in Europe; these were categorised as intercultural marriages.

The migrant spouses were examined according to the following aspects of their settlement experience: family situation, economic participation, economic wellbeing, health and satisfaction 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 the first and second interviews. Economic participation was indicated by their employment status while economic wellbeing was examined in terms of their reported weekly income and housing occupancy status.

Comparisons were made between migrants in intracultural marriages and those in intercultural marriages. 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 and intermarried migrants, multivariate statistical analyses were carried out to investigate the factors correlated with positive or negative outcomes. Since the outcome variables were usually dichotomous, loglinear analysis using the CATMOD procedure in SAS was the method 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 were usually the top four sources of spouse migration to Australia (Khoo, 2001). China was briefly the top source country in 1995 to 1996 when Chinese nationals who had been temporary residents after the Tiananmen Square incident in 1989 were granted permanent resident status in November 1993 and were able to sponsor family members for immigration. Table 3 shows the country of origin of the Asian-born spouse migrants in this study. The largest number of spouse migrants came from Vietnam, which accounted for one-quarter of all Asian-born spouses who migrated during the two-year period of the survey. Large numbers of migrant spouses also came from Philippines, China and India. These four countries accounted for nearly 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 of

respondents Weighted population

Intra-cultural Marriage

(%)

Female (%)

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

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Spouse migration from Asia to Australia is predominantly female. Nearly four out of five spouse migrants from Asia in recent years were women. The resulting sex ratio of about 27 males 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 Asian countries 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 it was still about two-thirds.

The proportion married to partners from the same cultural background varied enormously by country of origin. Almost all spouse migrants from Vietnam were married to or migrating to marry someone also born in Vietnam. The proportion in intracultural marriages was also more than 90% for migrant spouses from Cambodia, Hong Kong, Taiwan and most South Asian countries. However, about half of all spouse migrants from Indonesia, Singapore and Philippines were in intercultural marriages and the proportion in intercultural marriages was highest for migrants from Japan, followed by those from Thailand.

Figure 16 shows the birthplace of the Australian resident partners of the migrants: for those in intracultural marriages, whether the partner was from the same country or same region of origin; and for those in intercultural marriages, whether the partner was born in Australia or elsewhere (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 or married to partners from the same country among spouse migrants from Afghanistan, Sri Lanka, 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%

Vietna

m

Afghan

istan

Sri Lan

kaInd

iaKore

a

Cambo

dia

Hong K

ong

Total

Indon

esia

China

Malaysi

a

Philipp

ines

Taiwan

Singap

ore

Thaila

ndJap

an

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 the same region and therefore likely to be of the same cultural background. This was also the case for a significant proportion of spouse migrants from China.

Migrants from Japan had the highest proportion marrying or married to Australian-born spouses. Next were migrants from Thailand, followed by those from Singapore and Philippines. 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 and those 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 in intercultural marriages (Table 4). Spouse migrants were generally younger on average than other non-spouse migrants from Asian countries. This was because about one-third of them were migrating as prospective spouses 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 and elderly parents who had been sponsored by their children. A much lower proportion of intramarried migrants said they could speak English well compared to intermarried migrants. This was not surprising as intramarried migrants were likely to speak their native language within the family. For the large proportion not able to speak 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 is important. The proportion speaking good English was higher among intermarried migrants than other Asian non-spouse migrants. This was true of both men and women. This would suggest that intermarried migrants would be the best placed among the three groups in terms of social and economic integration.

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Table 4 Background Characteristics of Migrant Spouses by

Type of Marriage and Sex Characteristics of Males Females migrant spouse Intra-

marriage Inter-

marriage Other Intra-

marriage Inter-

marriage Other

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 60.0 migration: % 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 5943 Source: 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 of education, the difference was not statistically significant. A higher proportion of intermarried women than intramarried women had post-school qualifications, but the opposite pattern was observed for men. Spouse migrants—both men and women—were not as well qualified as other non-spouse migrants from Asia.

Intermarried men were the most likely to be employed before migration. But there was not much difference in the proportion employed before migration among the three groups of female 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 in Australia. Most intermarried male migrants had also visited Australia before migration, and the male spouse migrants were in general much more likely to have visited Australia before migration than the female migrants. This would have been helpful to them when they migrated as it would not be the first time that they had been in Australia. In contrast, most of the women in intracultural marriages had not been to Australia before migrating.

Intramarried spouses also had a lesser role in the decision making process about their migration compared with intermarried spouses, particularly among the women. Half of all women in intracultural marriages reported that it was their spouse’s idea that they migrated compared with less than one-quarter of women in intercultural marriages. Less than one in five 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 be more disadvantaged in their settlement and adjustment process compared with women in intermarriages and male spouse migrants generally. They were likely to be more isolated from the wider community (because of their inability to speak good English) and less likely to have any relatives in Australia. For most of them, their migration was the also first time that they had set foot in Australia and the idea of their migration had been not been theirs, but their husband’s.

Settlement Outcomes

The settlement experiences of the spouse migrants were examined at about 6 months and 18 months 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 participation in 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 sphere since their spouse was from a different cultural background, but their adjustment in the social sphere 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 at the 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% for women but lower for men (Table 5). Among the men, the proportion still married was also lower among intramarried migrants than intermarried migrants, but the difference was too small to be statistically significant. The proportion married was also lower among male migrant spouses than other male (non-spouse) migrants. It would appear that male spouse migrants might have more difficulty adjusting to marriage and migration than female spouse migrants.

About similar proportions of male and female spouse migrants had children at the time of the first interview soon after arrival (Table 5). However, only 9% of all male migrants reported having a child between the first and second interviews. In contrast, nearly 30% of intramarried female migrants and 18% of intermarried female migrants reported having a child during the one-year period between the two interviews. These data suggest again that the female migrants might be adjusting to their new family situation better than the male migrants. The lower proportion of intermarried women having children after migration might suggest that they had more adjustment to make in their marriage than intramarried women, although it might also be related to other aspects of their settlement, such as greater participation in the work force.

When compared with other Asian migrants, the spouse migrants were less likely to have children at the first interview but more likely to have a child between the first and second interviews. A high proportion of other Asian migrants had children at the time of migration because many migrants in the skill or business migration categories were accompanied by their families (spouses and children) when migrating to Australia. In contrast, spouse migrants were more likely to be recently married or migrating to marry in Australia; therefore many would not have had any children at the first interview.

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Table 5 Settlement Outcomes at 6 Months (W1) and 18 Months (W2) After Arrival

Males Females

Intra-

marriage Inter-

marriage Other Intra-

marriage Inter-

marriage Other

FAMILY SITUATION % % % % % % Married at W1 89.4 92.0 99.6 97.7 97.6 97.0 Had children at W1 27.3 15.0 39.0# 21.3 23.3 71.8# Had child W1-W2 8.6 8.6 5.3 29.4 17.8 6.7# Relatives in household at W1 48.3 40.9 36.8 42.2 12.1* 57.1# Relatives in household at W2 48.1 29.9* 30.7# 35.1 8.5* 48.6# ECONOMIC STATUS Weekly income <$300 at W1 47.8 27.8 54.5 87.4 78.2* 69.9# Weekly income < $300 at W2 27.2 36.2 33.1 68.2 54.5* 44.1# Owned/buying home at W1 13.5 10.8 6.9 11.4 34.2* 3.2# Owned/buying home at W2 14.9 17.3 15.0 22.1 60.8* 12.4# HEALTH Health good at W1 92.3 100.0 90.6 82.0 87.7 87.8 Health good at W2 87.0 100.0 89.5 78.9 88.5 79.8 ECONOMIC PARTICIPATION Employed at W1 47.1 74.6 41.0 12.7 21.8* 23.0# Employed at W2 66.9 66.8 56.9 23.2 38.1* 38.4# Unemployed at W1 35.6 14.4 31 19.8 13.1 18.8 Uenmployed at W2 10.7 29.9 18.9 12.7 9.0 11.2 Studying at W1 16.0 8.9 16.4 20.8 5.9 21.5 Studying at W2 19.9 1.3 14.9 15.8 7.7 19.8 Not employed/studying W1 1.3 2.0 11.5 46.7 59.3 36.7 Not employed/studying W2 2.5 2.0 9.3 48.4 45.2 30.5 SATISFACTION WITH LIFE IN AUSTRALIA Satisfied at W1 85.7 87.6 84.8 89.4 84.5 80.7# Satisfied at W2 81.8 66.2 85.9 92.3 87.8 84.7 Source: 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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There was no significant difference among the three groups of male migrants examined in Table 5 in terms of whether they were living in households with other relatives at 3 to 6 months after arrival, but differences were observed one year later. Significant differences were observed among the three groups of female migrants in their living arrangements at both points in time. A number of male migrant spouses appeared to be living with their wives’ extended families soon after arrival with over 40% reporting having other relatives in the household. The percentage with relatives in the household declined considerably among intermarried male migrants by the second interview, but there was little change among intramarried males. In contrast with male intermarried migrants, few women in intermarriages lived in extended family households after arrival in Australia. Most of them appeared to be in nuclear family households, consisting of themselves and their partner (and children) and no other relatives.

The proportion living with other relatives declined between the first and second interviews for all groups of migrants shown in Table 5 except intramarried males, indicating that many couples might have set up their own households after initially living with their relatives. However, nearly half of intramarried males and one-third of intramarried females were still living in extended family households more than a year after arrival.

Income and Housing Status

The two measures of economic wellbeing examined here were the migrant’s income and housing status. A weekly income of less than $300 at the time of the survey was an indication that a person had little earned income and was likely to be receiving social security benefits. This level of income is also an indicator of economic independence since a person who has an income less than this amount is likely to be less independent economically than one who has higher income. Housing status was examined according to whether the migrant was living in housing that was owned or being purchased by the family.

A greater proportion of intramarried migrants than intermarried migrants had income of less than $300 a week at the first interview. About half of the intramarried men had an income of less than $300 a week, compared with about a quarter of intermarried men. An even greater proportion of female migrant spouses had incomes of less than $300 a week, with the proportion being highest (87%) among intramarried women. While a lower proportion of male spouse migrants than non-spouse migrants had low incomes, the reverse was the case among female migrants. Female spouse migrants appeared to be significantly worse off than other female Asian migrants in terms of income.

One year after the first interview, fewer migrants in all groups except intermarried male migrants reported an income of less than $300 a week. The proportion increased among intermarried men, probably because their employment rate decreased and their unemployment rate increased between the two interviews. At the second interview, the proportion with low

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incomes was still higher for female spouse migrants compared with non-spouse migrants; it was also persistently higher among intramarried women than intermarried women.

Further analyses of the data showed that, as expected, there was a high correlation between having a weekly income of less than $300 and not being employed, and that the differences in income between intramarried and intermarried migrants were due to their employment status. Similarly the changes in the proportion with low income between the two interviews were related to the changes in their employment and unemployment status.

The second indicator—home ownership—indicates that the individual or family has an important financial asset. People who own their homes are usually in a better economic position than those who are living in rented housing. It is expected that home ownership among migrants will increase with longer duration of residence as they become more settled and accumulate enough financial resources to buy a house. Although the spouse migrants were all recently arrived immigrants, their spouse who sponsored their migration had either lived in Australia for sometime or Australian-born and some of them were likely to own their homes. The housing status reported by the spouse migrants therefore refers to the couple’s housing tenure.

There was no significant difference in the rate of home ownership at the first interview between intramarried and intermarried male migrants. About 10% of both groups reported living in homes that were owned or being bought. However, there was a significant difference between intramarried female migrants and intermarried female migrants. The latter had a home ownership rate that was three times that of the former. The higher proportion of intermarried female migrants living in homes that were owned or being bought was probably a reflection of their spouses being native-born Australians or had lived in Australia for a long time, and therefore had accumulated enough resources to own or buy a house.

At the time of the second interview a year later, the rate of home ownership had increased for all spouse and non-spouse migrants. However, the increase was greater in some groups that in others. The group showing the least increase was the intramarried male spouse migrants. The proportion living in their own home doubled for intramarried females between the two interviews. There was also a large increase for intermarried female migrants and they (and their husbands) had the highest rate of home ownership at the second interview.

Health Status

A very high proportion of male migrants reported having good health and there was no significant difference between intramarried and intermarried men, and between them and non-spouse migrants. Women were less likely than men to indicate that their health was good. Nonetheless, over 80% said that they were in good health. There was also no significant

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difference between intramarried and intermarried women, and between them and non-spouse migrants.

There was not a lot of change in the percentage with good health among both men and women one year after the first interview.

Participation in Employment or Education

How well the migrants adjust in the social sphere was examined according to their participation in employment or education. Intermarried migrants were more likely to be employed soon after arrival than intramarried migrants. The difference between male migrants was not large enough to be statistically significant; however, the proportion employed among intermarried women was nearly twice that of intramarried women. The highest proportion employed were intermarried male migrants and the lowest proportion employed were intramarried female migrants. A year later, there was an increase in the percentage employed and a decrease in the percentage unemployed in all groups except intermarried male migrants, who appeared to suffer a decrease in employment and an increase in unemployment.

Multivariate analyses were carried out to investigate what it was about intermarried couples that were associated with their greater likelihood of being employed—whether it was because of their better English skills, level of education or other attributes. The results showed that a spouse migrant’s likelihood of being employed at both times was highly correlated with the ability to speak good English, but not level of education (Table 6). The results also confirmed that men were more likely to be employed than women regardless of whether they were in intracultural or intercultural marriages. Although being intermarried was significantly associated with a greater likelihood of being employed, this association became insignificant when account was taken of whether the migrant had visited Australia before migration (Model 2). This indicated that the difference in employment status between the two groups of spouse migrants was due to intermarried migrants being more likely to have visited Australia before migration than intramarried migrants. Having visited Australia before migration appeared to have helped the employment prospects of intermarried migrants, probably because it might have provided useful information about the labour market.

Intramarried migrants were more likely to be enrolled in further studies than intermarried migrants. This might be to improve their employment prospects. A similar proportion of non-spouse migrants was also enrolled in further studies and there was not much change a year later in the proportion studying. Very few of the men were neither in the work force nor studying, as expected. However, about half of all female migrant spouses were neither in the work force nor enrolled in education and presumably were housewives.

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Table 6 Maximum Likelihood Estimates of the Effects of Migrant Characteristics on Employment Status at 6 Months (W1)

and 18 Months (W2) After Arrival. Employment status at W1 or W2 Variable Employed at W1 Employed at W2 Model 1 Model 2 Model 1 Model 2 Intramarried -0.3077* -0.1751 -0.2346* -0.1248 Intermarried 0.3077* 0.1751 0.2346* 0.1248 Sex Female -0.8275* -0.8317* -0.9203* - 0.9230* Male 0.8275* 0.8317* 0.9203* 0.9230* Spoke good English Yes 0.4715* 0.3581* 0.4652* 0.3668* No -0.4715* -0.3581* -0.4652* -0.3668* Education Degree or higher 0.0838 0.0580 0.0320 0.0048 Vocational qualifications 0.0599 -0.0099 0.0088 -0.0524 No qualifications -0.1437 -0.0469 -0.0408 0.0476 Visited Australia before migration Yes 0.5819* 0.4642* No -0.5819* - 0.4642* Likelihood ratio chi-square 14.8 30.95 16.76 30.79 Df 17 36 17 36 Number of respondents 612 612 524 524 *p<0.05

Satisfaction with Life in Australia

Survey respondents were asked about their level of satisfaction with life in Australia at each interview. Their response might be viewed as their own assessment of their adjustment in both the personal and social spheres. At the first interview, at least 85% of male and female spouse migrants said that they were satisfied with their life in Australia. There were no significant differences between intramarried and intermarried migrants. Interestingly, female spouse migrants were more satisfied with their life in Australia than female non-spouse migrants.

The proportion who were satisfied with their life at the second interview was not very different from the proportion at the first interview for all groups except intermarried male

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migrants. For this group, there was a sharp drop in the level of satisfaction and this was likely to be related to the drop in their employment rate and the sharp increase in their unemployment rate between the two interviews. Differences in the level of satisfaction at the second interview between intramarried and intermarried migrants were also not significant.

Conclusion

This study of Asian-born migrants in intracultural and intercultural marriages in Australia shows that the two groups of migrants differ in several important ways in their socioeconomic and migration background that in turn resulted in differences in their adjustment and settlement experiences after migration. The most important differences in their background characteristics appeared to be their level of English proficiency and whether they had visited Australia prior to migration. Intramarried migrants were on average not as proficient in English as intermarried migrants. They were also less likely to have visited Australia before migration. These two factors were important in influencing their employment status and income after migration.

Intramarried migrants also appeared to be less likely than intermarried migrants to initiate their migration process or participate jointly with their sponsoring partner in the decision-making process about their migration. For a significant proportion of the cases and particularly among female migrants, it was the sponsoring partner’s idea that the spouse should immigrate, suggesting that the migrant spouse might be in a subordinate position regarding her own migration process. The low English proficiency and lack of economic independence—as indicated by the large proportion with low income—among intramarried female migrants would further suggest that they were also more dependent on their husbands after migration.

In contrast, migrants in intercultural marriages were more likely to be a participant in their migration decision. Most of them were able to speak good English and many had been to Australia before migrating, two factors that appeared to facilitate their participation in employment soon after migration. The results of the data analysis confirm the hypothesis that intermarried spouse migrants adjust more quickly to Australian society in terms of economic participation than intramarried spouse migrants. Their better English language skills would also indicate that they were able to communicate better with and participate in the wider community.

There was no evidence that migrants in intercultural marriages might have more difficulty than migrants in intracultural marriages in their marital relationship during the first two years after migration. However, male migrants spouses were less likely than female migrant spouses to be still married 18 months after arrival and to have a child during this period. Female intermarried migrants were also less likely than intermarried migrants. Interestingly,

43

female spouse migrants were more satisfied with their life in Australia than female non-spouse migrants.

The proportion who were satisfied with their life at the second interview was not very different from the proportion at the first interview for all groups except intermarried male migrants. For this group, there was a sharp drop in the level of satisfaction and this was likely to be related to the drop in their employment rate and the sharp increase in their unemployment rate between the two interviews. Differences in the level of satisfaction at the second interview between intramarried and intermarried migrants were also not significant.

Conclusion

This study of Asian-born migrants in intracultural and intercultural marriages in Australia shows that the two groups of migrants differ in several important ways in their socioeconomic and migration background that in turn resulted in differences in their adjustment and settlement experiences after migration. The most important differences in their background characteristics appeared to be their level of English proficiency and whether they had visited Australia prior to migration. Intramarried migrants were on average not as proficient in English as intermarried migrants. They were also less likely to have visited Australia before migration. These two factors were important in influencing their employment status and income after migration.

Intramarried migrants also appeared to be less likely than intermarried migrants to initiate their migration process or participate jointly with their sponsoring partner in the decision-making process about their migration. For a significant proportion of the cases and particularly among female migrants, it was the sponsoring partner’s idea that the spouse should immigrate, suggesting that the migrant spouse might be in a subordinate position regarding her own migration process. The low English proficiency and lack of economic independence—as indicated by the large proportion with low income—among intramarried female migrants would further suggest that they were also more dependent on their husbands after migration.

In contrast, migrants in intercultural marriages were more likely to be a participant in their migration decision. Most of them were able to speak good English and many had been to Australia before migrating, two factors that appeared to facilitate their participation in employment soon after migration. The results of the data analysis confirm the hypothesis that intermarried spouse migrants adjust more quickly to Australian society in terms of economic participation than intramarried spouse migrants. Their better English language skills would also indicate that they were able to communicate better with and participate in the wider community.

44

There was no evidence that migrants in intercultural marriages might have more difficulty than migrants in intracultural marriages in their marital relationship during the first two years after migration. However, male migrants spouses were less likely than female migrant spouses to be still married 18 months after arrival and to have a child during this period. Female intermarried migrants were also less likely than female intramarried migrants to have a child during the first two years after migration. This might be related to the difference in their employment status, although there was not much difference between the two groups in the proportion who were neither in the labour force nor studying.

The main difference in their family situation was one that was likely to be related to differences in their cultural background. Intramarried migrants were much more likely to live with other extended family members than intermarried migrants. Female intermarried migrants were least likely to live in extended family households and most likely to live in nuclear family households. Many Asian migrants come from cultural backgrounds where extended family households are common. In contrast, multifamily households are less common in Australian society. Therefore, migrants in intercultural marriages are less likely to have other relatives living in the same household as themselves. It is also usual for recently arrived migrants, particularly those with relatives in Australia, to share accommodation with extended family members or friends (VandenHeuvel and Wooden, 1999) during the initial settlement period while they look for employment and more permanent housing arrangements.

In spite of the differences observed between intramarried and intermarried migrants in their family and economic circumstances during the first two years after their arrival in Australia, there was no difference in their health status and their level of satisfaction with life in their new environment. Most reported themselves to be in good health and to be satisfied with their life in Australia. Each group appeared to be satisfied with their different living arrangements and the level of their social and economic participation.

As to the question of whether spouse migrants differ from non-spouse migrants and, therefore may require specific settlement assistance, the conclusion is that there are significant differences between the two groups in many of their demographic and socioeconomic characteristics. The group of non-spouse migrants was a mixture of skill, business and humanitarian migrants (which included refugees). They tended to be older and better qualified than the spouse migrants. More importantly, the non-spouse migrants were also more likely to be responsible for the own migration decision and therefore were in a more empowered position than spouse migrants in the migration decision making process.

The above differences between spouse and non-spouse migrants and the settlement outcomes of spouse migrants—particularly those of female intramarried migrants—suggest that the provision of special assistance for spouse migrants may be justified. Such assistance can be in the form of pre-migration information as that provided by the Department of Immigration and Multicultural Affairs in its videos and booklet for potential spouse migrants. There is also a

45

need for post-arrival assistance with English language training, job search and opportunities for social participation outside their immediate family and ethnic community, particularly for migrant spouses in intracultural marriages.

This study of migrants in intra- and intercultural marriages is the first to examine their initial experiences after migrating to Australia to marry or be reunited with partners who have sponsored their immigration. While the focus is on Asian-born migrants, the issues that emerge in terms of their migration and settlement patterns may also apply more generally to other migrants who are migrating to marry or be reunited with partners of the same or different cultural background.

46

References Birrell, B. (1995) ‘Spouse migration to Australia’, People and Place, 3(1), pp. 9–16.

Cahill, D. (1990) Intermarriages in International Contexts: A Study of Filipina Women Married to Australia, Japanese and Swiss Men, Scalabrini Migration Centre, Quezon City.

Chuah, F., Chuah, L. D., Reid-Smith, L. and Rice, A. (1987) ‘Does Australia have a Filipina bride problem?’, Australian Journal of Social Issues, 22(4), pp. 573–583.

Cooke, F.M. (1986) ‘Australian-Filipino marriages in the 1980s: The myth and the reality’, Australian-Asian Papers No. 37, Centre for the Study of Australian-Asian Relations, Griffith University.

Department of Immigration and Multicultural Affairs (1997) The Migrant Experience: Wave One, Longitudinal Survey of Immigrants to Australia, Canberra.

Iredale, R. (1994) ‘Patterns of spouse/fiance sponsorship to Australia’, Asian and Pacific Migration Journal, 3(4), pp. 547–566.

Khoo, S.E. (1997) Sponsors of Spouse Migration, Department of Immigration and Multicultural Affairs, Canberra.

Khoo, S.E. (2001) ‘The context of spouse migration to Australia’, International Migration, 9(1), pp. 111–131.

Penny, J. and Khoo, S.E. (1996) Intermarriage: A Study of Migration and Integration, Canberra: AGPS.

Smith, A. and Kaminskas, G. (1992) ‘Female Filipino migration to Australia: An Overview’, Asian Migrant, 5(3), pp. 72–81.

Vandenheuvel, A. and Wooden, M. (1999) New Settlers Have Their Say: How Immigrants Fare over the Early Years of Settlement, Department of Immigration and Multicultural Affairs, Canberra.

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Chapter 3

Migrated Household in Indonesia: An Exploration of the Intercensal

Survey Data

Salut Muhidin Demographic Institute University of Indonesia

Abstract

While research on Indonesian migration has been extensive, there have been few studies focused on family migration. The family as a social unit has a major contribution in making decisions related to migration, especially in developing countries (Root and De-Jong, 1991). Data from the 1995 Indonesian intercensal survey clearly documents this phenomenon by showing that more than 50% of internal migrants have cited marriage and family reunion, as well as economic, education, housing concerns as the primary triggers for migration. In addition, the survey provides information on family circumstances (such as family structure, family sources, and previous mobility experiences among family members) that allows for an analysis of migratory trends at the familial level. Adopting a model of family migration as developed by Root and De-Jong (1991), this paper aims to contribute to the study of family migration in Indonesia by exploring trends in family migration as documented in the intercensal survey data. However, the data only allows for the measurement of migrated households rather than family migration. Here, a migrated household is defined as a household in which at least one member is a migrant. A logistic regression model is utilised to explain the differentials in household migration by selected explanatory variables.

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Introduction

igration studies focusing on Indonesia (Tirtosudarmo, 1997; Hugo, 1999; Spaan, 1999) have concluded that population mobility or migration within and across Indonesia and

its neighbouring countries would likely become an increasingly important issue toward 21st century. Explanations provided for this trend may be explained by socioeconomic and political changes, globalisation processes1 transportation and communication improvements, and the proliferation of migration networks. For example, the recent policy on decentralisation or regional autonomy implemented in early 2001 may have an effect on family migration. The policy emphasises the regional dimension in Indonesia’s development, which positively influences regional economic development which, in turn, eventually exerts a significant impact on population mobility and distribution. As such, it will be important to examine population mobility in Indonesia by considering the family as well as the individual as the unit of analysis.

Unlike individual migration, family migration has received little attention among scholars of migration. A study of the family in the context of migration is necessary as the family plays a crucial role in triggering migration among Indonesians. For example, it is customary after marriage that a married man/woman follows his /her spouse to set up a household away from one’s previous residence. Alternatively, the couple may set up residence in a place different from their previous residences. Furthermore in Indonesia, a common practice is for family members to be economically dependent on the head of a household or parents2. As such, family reunions become common once the wage-earner within the family (the parents or the head of household) migrates. Thus, the phenomenon of chain migration (that is, migration by a person who follows the track of former migrants who may or may not be related) also appears among Indonesian migrants (Gooszen, 1999; Spaan, 1999).

It is for these reasons that the family remains a neglected unit of analysis coupled with the constraints presented by the lack of data. For example, the national census, probably the most potent source of migration data, is deficient of relevant data since it records only inter-provincial and permanent migration, while excluding intra-provincial or temporary movements of people. Nonetheless, the census data consists of information on family circumstances (for example, the numbers of family members, type of family, and family socioeconomic conditions), which is useful for analysis. Another data source is the intercensal survey. Although it has fewer samples than the census, the survey has more features related to migration variables3. Based on the above, the present study aims to contribute to the analysis 1 A basic assumption of globalisation is that national boundaries are fast decreasing as country borders become pervious, allowing foreign as well as global ideas, technology and goods to take root in receiving countries. 2 Here, the social security schemes that are prevalent in the Western societies do not exist in Indonesia. 3 For example, a question on the reason for migration has been addressed. In the 1995 survey, eight alternative reasons were addressed (that is, work, in search of jobs, education, marriage, followed by the family, relatives, housing, among other reasons). These variables have been incorporated into the analysis of family migration in Indonesia.

M

48

of family migration in Indonesia by examining the intercensal survey data. The study attempts to answer the question: “what is the probability of a household, either partially or entirely, migrating due to selected family backgrounds and characteristics?” In other words, the main focus of this study is the interactions among family characteristics and how they affect migration as revealed in the intercensal survey data4. The study also focuses on the link between family migration theory and the variables available in the data source.

It must be noted that the data source is limited in that it tends to focus on the household rather than the family. Hence, this present study examines if migration occurs among household members. Thus, this study is the first statistical analysis on migration by optimising the use of the data set in its attempt to provide insight into family migration in Indonesia. Therefore, this caveat should be borne in mind when interpreting the result of this study.

The paper is divided into five sections, the first of which is the Introduction. The second section, Theoretical Framework, presents major theories on family migration by contributing to the identification of research needs in the data to be analysed. Potential variables from the survey data and the methodology used are elaborated in the third section, Data and Method. This section also describes the analysis of the data. The empirical results from the data are elaborated in fourth section, Finding and Discussion. In the fifth section, the Conclusion, a discussion of the results as well as recommendations will be presented.

Theoretical Framework

Generally, migrants move for specific reasons. Sjaastad (1962), for example, states that an individual migrates in the expectation of being better off, expecting that his/her lifetime benefits are higher than perceived migration costs. People will migrate over a longer distance if the relative advantage of having relocated exceeds the costs of leaving their previous hometowns. In other words, the existence of a trigger or motive for migration is a necessary antecedent for a move to take place.

Evidence from the 1985 and 1995 intercensal surveys shows that family reasons (such as marriage and family reunion) is the main trigger for Indonesian people to migrate within the country (Figures 17 and 18). Economic reasons are the second most important reason for why people migrate. At the regional level, the proportion of migrants motivated by any migration trigger varied considerably. Comparable with popular opinion, education opportunities are highly developed in Java than other regions in Indonesia. Thus, migration triggered by education was found to be relatively higher among populations residing in regions outside Java.

This situation is somehow different when considering the trigger for international migration, especially labour migration. Spaan’s (1999) study revealed financial remittances more than 4 The intercensal survey is the most conventional data source.

49

family reunion (or the family trigger) to be responsible for international migration among Indonesians. International labour migration data also reveals that it is more likely that only a few members within the family migrate rather than the entire family.

Figure 17 Proportion of Migrants by Migration Triggers, Indonesia 1980-1985

Indonesia 1980-1985

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Indon

esia

Sumatr

aJa

va Bali

Nusa T

gr

Kaliman

tan

Sulawes

i

Maluku

IrianJ

aya

Family&otherTransmigrationEducationEconomic

Source: The 1985 Intercensal Population Surveys (SUPAS).

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Figure 18 Proportion of Migrants by Migration Triggers, Indonesia 1990-1995

Indonesia 1990-1995

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Indon

esia

Sumatr

aJa

va Bali

Nusa T

gr

Kaliman

tan

Sulawes

i

Maluku

IrianJ

aya

FamilyHousingEducationEconomic

Source: The 1985 and 1995 Intercensal Population Surveys (SUPAS).

The majority of migration studies focusing on migration decisions in Indonesia has adopted three approaches: the behavioural approach, the economic approach, and the structural approach. The behavioural approach (Wolpert, 1965) and economic approach (Todaro, 1980) view the process of migration decision making in terms of individuals making judgements or choices as a response to stresses posed by the environment or as a perception of the differential between current wage earning and expected wage earning in the future. This is similar to the theory of push-pull or equilibrium theory proposed by Lee (1966). According to Lee (1966), migration is defined as results of the interplay of factors stimulating and inhibiting movements associated with the origin and destination, together with intervening obstacles (for example, distance) and personal characteristics of the migrants. In both origin and destination areas, there are negative (push) and positive (pull) factors, both of which form the basis for decisions to stay or move.

Nevertheless, the approaches that focus on individual decisions have been challenged, especially in the context of Indonesia. Skeldon (1990) argues that this theory overemphasis economic, as well as objective and measurable factors for migration. Communities in Indonesia known to be highly mobile are the Minangkabau and Batak in Sumatra, Bugis and Makassarese in Sulawesi, Banjarese in Kalimantan, and Madurese in Java. Therefore, attention should also be paid to the normative systems of these societies and personal traits of the migrants. Migration decisions also depend on cultural and economic contexts. In the

51

Javanese context, for example, where mutual family deliberation (musyawarah) is common, decisions pertaining to education, marriage of children, work and migration are often undertaken by parents (Geertz, 1961; White and Hastuti, 1980).

A complementary model to the behavioural and economic approaches is the new economic approach, which was developed and used to support the family model of migration. The new economic approach (Stark, 1991) emphasises the risk-sharing behaviour of family members. According to this model, individuals are able to diversify their resources in order to minimise risks to the family income. Hence, it may be more viable for a few family members to work in foreign country, while allowing those left behind in the home country to receive remittances.

According to the new economic approach, migration occurs in order to improve the income of the household more than the economic situation of individual migrants. Compared with the network or structural approaches (Hugo, 1981; Masey, 1990), migration becomes a self-perpetuating process, especially when the costs and risks involved are lowered by social and informational networks. Existing network ties (such as the family, relatives, or social networks) lower the risks associated with migration to foreign regions because migrants can expect help from those who had previously migrated. This social phenomenon is known as chain migration.

Corner and Tirtosudarmo’s (1985) study, which used data from the 1985 National Migration Survey conducted in East Java, South Sulawesi and Bali, showed that migrants were relatively more educated than non-migrants, with higher proportions of them involved in professional status occupations, though these numbers were small. Many migrants move to urban areas for job-related reasons. While the first move is characterised by rural-urban individual migration, the last move is characterised by urban- urban family migration. Relatives and friends play a very important role in disseminating information about destinations in urban areas. Furthermore, from this data set, it is clear that unlike migrants who move mostly for economic reasons, non-migrants continued to dominate agricultural activities and rarely cited economic reasons for staying.

Working on these theoretical approaches on individual and family migration, Root and De-Jong (1991) developed an inclusive family model of migration in the context of developing countries. For Root and De-Jong (1991), family migration occurs either when the entire family migrates or when only a few members of the family migrate. In this model, six basic factors were generated to explain family migration. These are:

1. Linkage to migration system

2. Ties at place of origin

3. Family pressure

4. Family structure

52

5. Family socio-economic resources

6. Family previous mobility experience

The first factor is represented by exchanges, for example, information, social assistance, money and emotional supports between family/kin at point of origin and potential destination regions. Using the migration survey data of the Philippines, Root and De-Jong (1991) utilised remittance flows as a variable of this factor. The second factor includes inhibiting factors for predicting migration. Included in this factor is the information on perceived closeness to relatives in origin region. The third factor follows Hugo’s (1981) work, which argues that families either generate or impose restrictions on the expectations for migration of family members. As such, individual perception on family pressures to migrate is intrinsic to this concept. In the family structure factor, following Castilo’s (1979) typology, we adopted two types of family structures—the nuclear family and the extended family. Here, the assumption is that the nuclear family is more likely to migrate as an entire family than an extended family. Another assumption is that migration of some members is expected to be high during stages of major life-course transitions such as school and marriage. The fifth factor pertains to family socioeconomic resources, which include push or pull factors in migratory trends. For example, the lack of adequate land may push family members to find work elsewhere. The last factor considers previous experiences from some members of the family or the entire family as a significant triggerof new movements or repeated movements of some members of the family or the entire family.

Root and De-Jong (1991) discovered that in the context of the Philippines, a highly significant predictor of individual migration or entire family migration was linked to the form of remittance exchanges and the previous migration experiences of family members. Migrants were also seen to have higher educational levels and fewer parcels of land. Family pressure to migrate emerged as the most significant predictor of entire family migration. A similar discovery was made in Malaysia by Chandra (1985) who found that migration families in Malaysia differed from non-migrant families in age, education, labour force participation, occupational structure, housing conditions and ownership, and family income. Ethnicity as well as regional population composition also affected these patterns of differentials.

In order to develop a family migration model for Indonesia, this paper considers the six factors of family migration as developed by Root and De-Jong (1991). Due to data limitations, the discussion focuses on the “household” rather than the “family” as the unit of analysis. In this case, differences in the definition of a household and family are significant. While a family consists of members who are related by blood ties, members of a household need not be related through consanguineal nor affinal ties.

53

Data and Method

The primary data source in this paper is the 1995 intercensal population survey (Survai Penduduk Antar Sensus, SUPAS). The 1995 SUPAS, which was held from September to October 1995, covered all geographical areas (a total of 27 provinces5) in Indonesia.

Migration Status of Household

The SUPAS data was utilised as it contained detailed information on households as well as the relationship of each household member to the head of the household. Combining this information with the status of the migrant from each household enabled us to examine the phenomenon of household migration.

The migration status of a household member is obtained by comparing the place of residence of the migrant at the time of survey with the migrant’s place of residence 5 years ago. In the 1995 SUPAS, a change of residence was recorded when a person had been absent from home for 6 months or longer or had left home for the purpose of moving away even when the 6 month limit had not been reached. For children below 5 years of age at the time of survey, migration status is then obtained by comparing the place of residence at the time of survey with the place of birth.

In this study, a household is defined as a migrated household if at least one member of the household is defined as a migrant. Among 948,380 people from 216,946 households who were interviewed, there were about 73,590 individuals who were migrants. Among the households chosen for this study, 182,439 (84%) were non-migrated households, while 34,507 (16%) were migrated households.

Explanatory Variables

In the SUPAS, characteristics of migrants such as age, sex, origin-destination regions, reason for migration, marital status, education and employment status were recorded. The socioeconomic circumstances of a household (e.g., ownership of dwelling unit, land for agricultural work, and availability of media information) were also collected. Combining both individual and household characteristics, we succeeded in delimiting four out of six factors in the family migration system as developed by Root and De-Jong (1991): family pressure, family structure, family socioeconomic resources and previous mobility experience. The other factors of linkage to migration system and ties at place of origin were difficult to capture from the data source. 5 Prior to September 1999, Indonesia consisted of 27 provinces, including East Timor. Since then, East Timor attained independence. In this paper, the 27 provinces are clustered into seven regions based on their geographical positions (see Figure 17).

54

Household Pressure

Household pressure is defined as the number of household members who migrated due to family triggers (such as marriage and family reunion). From the 1995 SUPAS data, the value of this variable ranges between 0 (none) to 11 household members, wherein about 63% of migrated households migrated due to family reasons. It is worth mentioning here that the question on migration triggers was only asked of migrants or members of migrated households. Therefore, the household pressure of all non-migrated households (that is, households that ever migrated) is always 0, while the actual numbers are 182,439.

Household Structure

Two variables are utilised to define household structure: size and composition of households. In terms of household size, it varies from a household comprising only a single member to a big household with 20 members. In this analysis, the variable is treated as a continuous variable. From 216,946 interviewed households, about 11,729 (5%) were households belonging to single-member household. More than 50% of the interviewed households consisted of 2 to 5 members. These are about 11%, 19%, 21% and 17%, respectively, for 2, 3, 4 and 5 members. The rest of the households (comprising 27%) have 6 and more members.

This paper considers four kinds of household composition. They are:

1. Households consisting of head of household alone with or without spouse

2. Households consisting of head of household and unmarried/single child(ren)

3. Households consisting of head of household, spouse, and also unmarried child(ren)

4. Households consisting of head of household and other extended household members

Previous Mobility Experience of Households

The mobility experience of household members is linked to the concept of lifetime migration. A person is defined as a lifetime migrant if he/she was not living in his/her place of birth at the time of the survey. It other words, the lifetime migrants in the households are those who have migrated at least once in their lifetime. Although the same definition is applied to children aged from 0 to 4 years, the concept of mobility experience is not applied to this age group.

55

Socioeconomic Resources of Households

The socioeconomic resources of households are determined by five factors: education, land ownership, dwelling ownership, household members who are involved in farming, and access to media information.

It was found that Indonesian families, on average, have 6.24 mean years of schooling, while the minimum and maximum values of this variable are 0 and 15.28 years. Household education is defined as the numbers of years of education completed by the adult members of a household. Here, family members aged 18 years and above fall into this category. Suppose a household comprises two adults and three children under 18 years of age, the mean number of years of schooling is estimated as the sum of years of schooling completed by adults divided by two (assuming that the two adults refer to the couple alone excluding their children). In addition, it is assumed that none may spend more than a year in the same grade.

Using logistic regression analysis, the mean years of completed schooling has been clustered into five groups. These are 0 years (no school), 1–5 years (yet to complete primary school), 6–9 years (completed primary school), 10–13 years (completed high school), and 14 years and above (university). When this variable is grouped, this shows more insight than using a continuous variable to interpret the results from the logistic regression model. The group values can capture the variation of different mean years of school to different type of migrated households, while it is less plausible in the continuous variable.

Other household socioeconomic variables (such as dwelling and land ownership and media information) are grouped into several categories, which are spelt out in Table 7.

Regional Dimension

Considering the fact that Indonesia is a heterogenous country, the regional dimension has also been included as an explanatory variable. Operational definitions of all selected variables analysed in this paper is summarised in Table 7.

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Table 7 Operational Definitions of Variables Considered for the Analysis of Migrated Households, Indonesia 1995

Variables Operational Definition

Dependent variable Migrant status Binary variable, which is 1 for migrated households and 0 for non-

migrated households. Independent variables 1. Household pressure to

migrate

Number of household members who migrate triggered by family reasons (such as, marriage and family reunions). The value ranges from 1 to 11.

2. Household structure - Household size - Household type

Number of household members range from 1 to 20 1. Households consisting of head of household alone with/without

spouse 2. Households consisting of head of household and unmarried/single

child(ren) 3. Households consisting of head of household, spouse and unmarried

child(ren) 4. Households consisting of head of household and other extended

family members 3. Household experience

Number of lifetime migrants in a household (lifetime migration here refers to the place of current residence that is different from the place of birth)

4.Socio-economic resources - Household education - Land ownership

- Households that farm - Dwelling ownership

- Media information

Mean years of schooling completed for household members aged 18 and over 0 = Households having no land 1 = Households owning less than 1 hectare of land 2 = Households owning less than 2 hectares of land 3 = Households owning more than 2 hectares of land Number of household members aged 18 years and above engaged in the agricultural sector 0 = self-owned or owned by instalments 1 = rent or contract-based 2 = others 0 = Households having no media information 1 = Households having a radio or television 2 = Households having both radio and television

5. Regional dimension Current place of residence Java = 0, Sumatra = 1, Bali = 2, Nusa Tenggara = 3, Kalimantan = 4, Sulawesi= 5, and Maluku-Irian Jaya = 6

57

Statistical Model

The logistic regression model was selected for the analysis since the dependent variable is dichotomous, meaning, the value 1 (migrated household) with a probability of success p, or value 0 (non-migrated household) with probability of failure 1-p. In general, the model applied is as follows:

...)(1

)(log 44332211 +++++=

−

xxxxxp

xp ββββα

where: p(x) = probability of being a migrated household controlled for X characteristics α = the constant of the equation, and β = the coefficient of the predictor variables Following Root and De-Jong (1991), family migration falls into two categories: families in which a few members migrate and those whose entire families migrate. From the data, 182,439 households (84% of all households) interviewed in the 1995 SUPAS belong to the category of non-migrated households. In addition, about 11% of the total number of households (23,156 in terms of numbers) have a few members who are migrants, while 5% of households (11,351) are those whose entire household consists of migrants. As a result, the dependent variable consists of more than two cases, namely—non-migrated, partially migrated and entirely migrated households. In practice, this range of variables allows us to use multinomial regression. For the purpose of this present study, the dependent variable is kept as a dichotomous variable. The two models applied in the analysis are as follows:

...)(1

)(log 414131312121111111

1

1 +++++=

−

xxxxxp

xpββββα

...)(1

)(log 42423232222212122

2

2 +++++=

−

xxxxxp

xpββββα

where: p1(x) = probability of being a partially migrated household p2(x) = probability of being an entirely migrated household Before embarking on a regression analysis of the data, it must be noted that a bivariate analysis has been applied already to the explanatory variables mentioned in Table 7. Based on this analysis, a selected number of explanatory variables have been selected and put into

58

logistic regression models. In order to understand the contribution of the variable of regional dimension to family migration, we have to divide the analysis into four models. In Model 1 and Model 3, regional dimension is not included as a control variable. On the other hand, Model 2 and Model 4 introduce the variable of regional dimension. While Models 1 and 2 represent partially migrated households, Models 3 and 4 represent entirely migrated households. This analysis will show the significant effect of household characteristics on the status of household migration.

Most information available in the 1995 SUPAS refers only to the situation at the time of survey or after the household has migrated. In reality, characteristics of a household and its members usually undergo substantial change during and especially after migration and, as such, few characteristics are unchangeable (apart from sex) or are more likely to follow a specific immutable course (apart from age). The longer the duration of residence of the migrant at the point of destination, the more likely their characteristics will become more removed from those at the time of migration. Considering the limitations presented in the data source, we have limited the logistic regression analysis to a few selected variables, and are assuming that these have not changed considerably within the five years prior to the time of the survey. The selected variables include the composition of households and its members, the mean number of years of completed school among the adults in a household, and land and dwelling ownership.

Finding and Discussion

Bivariate Analysis

The results of the bivariate analysis for all explanatory variables as defined previously are presented in Table 8. From the analysis, it is evident that these variables have a significant effect on the migration status of a household (that is, when none, partial, or entire households migrate).

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Table 8 Bivariate Analysis of the Selected Characteristics for Migration Status of Households, Indonesia 1995

Characteristics Non-migrated

Households Partially migrated households Entirely migrated households

(%) N (%) N LR P (%) N LR P A. Household (HH) pressures to migrate

HH members migrated due to (0) 0 (19) 21,786 72,536 0,000 (50) 17,718 43,389 0,000 family triggers

B. Household structure Total HH members (84) 797,938 (12) 114,785 3,767 0,000 (4) 35,657 5,375 0,000 Mean family size 4.37 4.96 3.14 Composition of HH 1. Head of HH with/without spouse (13) 23,004 (5) 1,120 10,771 0,000 (32) 3,664 3,657 0,000 2. Head of HH & single child(ren) (6) 11,629 (2) 424 (3) 354 3. Head of HH, spouse & child(ren) (58) 106,461 (38) 8,724 (36) 4,100 4. Extended family (23) 41,150 (56) 12,880 (28) 3,233

C. Socioeconomic resources Mean years of schooling for members 5.82 8.00 11,613 0,000 9.28 12,683 0,000 aged 18 years and above Adults working in agricultural sector (32) 149,630 (17) 12,234 5,260 0,000 (8) 1,857 7,132 0,000 Agricultural land ownership 1. No land ownership (49) 89,077 (68) 15,719 3,060 0,000 (88) 10,038 7,637 0,000 2. Owning less than 1 hectare of land (27) 49,889 (17) 3,942 (5) 607 3. Owning less than 2 hectares of land (13) 24,161 (8) 1,857 (3) 355 4. Owning more than 2 hectares of land (11) 19,312 (7) 1,638 (3) 351 Dwelling unit ownership 1. Self-owned or owned through instalments (87) 158,207 (68) 15,686 4,911 0,000 (28) 3,156 20,652 0,000 2.Owned through contract or rent (9) 15,560 (23) 5,421 (64) 7,249 3. Others (5) 8,672 (9) 2,049 (8) 946 Media information available 1. Having no radio/television (28) 51,983 (18) 4,224 2,076 0,000 (22) 2,498 270 0,000 2. Having a radio or a television (37) 66,633 (32) 7,461 (37) 4,227 3. Having both radio and television (35) 63,823 (50) 11,471 (41) 4,626

D Previous mobility experience Household with previous experience: Yes (29) 53,759 (67) 15,493 12,097 0,000 (86) 9,706 14,490 0,000 No (71) 128,680 (33) 7,663 (14) 1,645

E. Regional dimension Sumatra (22) 39,558 (22) 5,159 48 0,000 (27) 3,064 343 0,000 Java (44) 80,535 (44) 10,157 (42) 4,808 Bali (3) 4,720 (2) 575 (2) 283 Nusa Tenggara (8) 14,858 (8) 1,740 (5) 576 Kalimantan (8) 14,670 (8) 1,899 (10) 1,082 Sulawesi (11) 20,254 (12) 2,772 (10) 1,165 Maluku and Irian Jaya (4) 7,844 (4) 854 (3) 373 Total Households (84) 182,439 (11) 23,156 (5) 11,351

Note: LR = Likelihood ratio value

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Household Pressure

Household pressures to migrate, which include the number of household members migrating due to family reasons, are found to be higher among entirely migrated households than partially migrated households. While half of the members of entirely migrated household cited family reasons for decisions to migrate, only 19% of members of partially migrated households mentioned the same reason. However, since the information on this variable is only available for migrants, it follows then that the variable of household pressure is not included in the logistic regression analysis.

Household Structure

The mean number of members in a household is significant for whether a few or the entire household migrates. The mean household size is 4 people for non-migrated households, 5 people for partially migrated households, and 3 people for entirely migrated households. It was found that the presence of a spouse is crucial in entirely migrated households. This is in contrast to partially migrated households, where there need not be the presence of a spouse. Since the variable for household size has been captured in the household composition, in the logistic regression analysis only the household composition is utilised instead of household size. Using this criterion, we assessed how different household compositions relate to migration, and whether these include partially or entirely migrated households.

Socioeconomic Sources of Households

The socioeconomic circumstances of households also have an effect on the probability of becoming a migrated household. With respect to education, non-migrated households have a lower number of mean years of schooling among adult household members than that of other households (whether they are partially or entirely migrated households). If the mean years of schooling can be converted into educational levels, it was found that the non-migrated households have an average education of primary school (which total to 6 years). In contrast, the average education among adults in partially migrated households is junior high school and the average in entirely migrated households is senior high school.

In general, the more members involved in the agricultural sector and the more land owned by the household means that the probability of becoming a migrated household is smaller. Among non-migrated households, about 32% of adult household members are engaged in agriculture while 51% own land for agricultural activities. This situation is different for migrated households. Among partially migrated households, only 17% of their adult household members work in the agricultural sector while 32% own agricultural land. The figures are 8% and 12%, respectively, for entirely migrated households. Nevertheless, in the regression model, the variable of work status is excluded, since it refers only to the situation at the time of the survey.

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The variable of land ownership is also included in the model. In reality, it may be possible that some households own land just after migration takes place. Hence, we need to assume that ownership of land among these households has not changed within five years prior to the time of the survey. The same assumption has been applied to the dwelling unit ownership variable, which has been found to be a significant coefficient.

The proportion of households that have or do not have media information (an approximate variable for information sources) was found to be slightly different depending on whether they are migrated households or not. From the data, about 28% of non-migrated households have no media information, whereas, the percentage is 18% for partially migrated households and 22% for entirely migrated households. In other words, access to media information through ownership of radio and/or television has less influence on the probability of a household becoming a migrant household, although it is statistically significant. This is due to the fact that the ownership of a radio or television has become common among Indonesians regardless of their socioeconomic status. People tend to view media as an entertainment tool, rather than a source of information. Therefore, this media information variable was not included in the logistics regression model.

Previous Mobility Experience of Households

There is great significance between previous mobility experience and the probability of being a migrated household. The greater the numbers of people who have migrated within a household, the higher is the probability of being a migrated household. It was found that 71% (128,680 households) of non-migrated households have no previous mobility experience. This figure is only 14% (1,645 households) among households where the entire household migrates. This factor will be examined further using regression analysis.

Regional Dimension

The regional composition of non-migrated households was found to be similar to partially migrated households. Differences in regional composition showed up with entirely migrated households, which were examined with logistic regression analysis.

Logistic Regression Analysis

Table 9 shows the results of the logistic regression analysis for partially and entirely migrated households compared with non-migrated households. In every model, all explanatory variables are statistically significant for increasing or decreasing the probability of a household becoming a migrated household, except for some regions. When the variable of regional migration is introduced to Models 2 and 4, this has little effect on other variables. In addition, having previous mobility experience among members in a household is more likely

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to increase the probability of a household becoming a migrated household. For this variable, statistically, partially migrated households are three times more likely to migrate compared to entirely migrated households, which tend to be five times more likely to migrate compared to a non-migrated household.

The probability of being a partially migrated household is higher among extended households than others. On the other hand, the probability of being an entirely migrated household for households with no child is the highest among the different types of household composition. If we see Model 4, for example, the odd ratio [exp (0.542)] is 1.719. This value means that a childless household has a 70% higher chance of being an entirely migrated household than an extended household. Once there are unmarried children in a nuclear household, the likelihood of being an entirely migrated household is lower. The probability here is even lower than for an extended household. In other words, the presence of unmarried children restrains a household from migrating. The educational level of children or those engaged in school activities may be a reason restraining a household from migrating. In the case of extended households (with siblings, parents or other relatives), the presence of other household members is sufficient condition for a household to move, although it has unmarried children.

In comparison with households where members are less educated or have no schooling, the results are similar to the bivariate analysis, where households with more educated members are more likely to become migrated households. Here, the probability of being a partially migrated household where members have the highest education is eight times higher than for households with non-schooling members. Once the regional dimension variable was considered such as in Model 2, the probability of being a partially migrated household increases, the odd ratio increasing to 8.7. Hence, households comprising of members with the highest education are 6 times more likely than those with members with no schooling of being an entirely migrated household. In other words, educational level is said to facilitate migration because it increases employment opportunities.

The ownership of agricultural land and dwelling units are also significant factors associated with the probability of being a migrated household. Households owning more land are less likely to migrate. The probability of becoming a partially or entirely migrated household for households that own no land or less than 1 hectare of land is higher than households that own more land. Households that rent dwelling units are more likely to be migrated households than households that self-own dwelling units. The probability of being a partially migrated household for households that rent houses is two times higher than for households that own houses. The ratio of the probabilities is much higher for entirely migrated households.

The probability of being a partially or entirely migrated household is higher in Nusa Tenggara and Sulawesi than in other regions. The probability of being a partially or entirely migrated household in Maluku and Irian Jaya is not statistically different from that of Java. Furthermore, the probability of being a partially migrated household in Sumatra compared to Java is not statistically different.

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Table 9 Logistic Ranalysis for Being a Migrated Household, Indonesia, 1990-1995

Partially migrated/Non-migrated Entirely migrated/ Non-migrated Variables Model 1 Model 2 Model 3 Model 4

b Exp.(b) b Exp.(b) b Exp.(b) b Exp.(b)

Previous mobility experience No 0.000 1.000 *** 0.000 1.000 *** 0.000 1.000 *** 0.000 1.000 *** Yes 1.101 3.008 *** 1.128 3.091 *** 1.752 5.764 *** 1.774 5.892 ***

Composition of HH Extended households 0.000 1.000 *** 0.000 1.000 *** 0.000 1.000 *** 0.000 1.000 *** Head of HH with/without spouse -1.761 0.172 *** -1.743 0.175 *** 0.523 1.687 *** 0.542 1.719 *** Head of HH and single child(ren) -1.984 0.138 *** -1.970 0.139 *** -0.474 0.622 *** -0.464 0.628 *** Head of HH, spouse and single child(ren)

-1.457 0.233 *** -1.441 0.237 *** -0.731 0.481 *** -0.726 0.484 ***

Mean years of completed schooling

No Schooling 0.000 1.000 *** 0.000 1.000 *** 0.000 1.000 *** 0.000 1.000 *** 1 - 5 years 0.944 2.569 *** 0.997 2.710 *** 0.588 1.799 *** 0.595 1.813 *** 6 – 9 years 1.486 4.419 *** 1.551 4.717 *** 0.925 2.521 *** 0.944 2.571 *** 10-13 years 1.831 6.238 *** 1.889 6.614 *** 1.455 4.285 *** 1.468 4.341 *** 14+ years 2.112 8.268 *** 2.159 8.663 *** 1.909 6.752 *** 1.900 6.687 ***

Land ownership Owning no land 0.000 1.000 *** 0.000 1.000 *** 0.000 1.000 *** 0.000 1.000 *** Owning less than 1 hectare of land 0.058 1.060 ** 0.043 1.043 * -0.413 0.662 *** -0.466 0.627 *** Owning less than 2 hectares of land -0.883 0.915 ** -0.162 0.851 *** -0.429 0.651 *** -0.572 0.564 *** Owning more than 2 hectares of land -0.121 0.886 *** -0.181 0.835 *** -0.336 0.715 *** -0.478 0.619 ***

Dwelling ownership

Self-owned 0.000 1.000 *** 0.000 1.000 *** 0.000 1.000 *** 0.000 1.000 *** Contract or rent 0.706 2.026 *** 0.700 2.015 *** 1.991 7.325 *** 1.973 7.193 *** Others 0.942 2.566 *** 0.933 2.543 *** 1.369 3.932 *** 1.326 3.764 ***

Current residence Java 0.000 1.000 *** 0.000 1.000 *** Sumatra -0.005 0.995 0.228 1.256 *** Bali 0.142 1.153 * 0.238 1.269 ** Nusa Tenggara 0.455 1.576 *** 0.544 1.722 *** Kalimantan 0.083 1.087 * 0.346 1.414 *** Sulawesi 0.192 1.211 *** 0.410 1.507 *** Maluku + Irian Jaya 0.002 1.002 -0.031 0.969 Constant -3.159 *** -3.288 *** -5.189 *** -5.354 *** χ2 26761 27011 28956 29169 Degree of freedom 13 19 13 19

Note: The stepwise forward conditional method was used for the logistic regression analysis, ***p < 0.001, **p < 0.01, *p < 0.05.

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Conclusion

This paper has shown that empirical studies on migrated households in Indonesia can be done by exploring data from the intercensal survey. The survey provides micro data on individual migration as well as the characteristics and socioeconomic sources of household members. At least four factors spelt out in Root and De-Jong’s family migration model were captured in the data. These are family pressure, family structure, family socioeconomic resources, and family previous mobility experience. Given that Indonesia is geographically differentiated, this paper also considers regional differences when analysing family migration.

The logistic regression analysis in this study found that household previous mobility experience, household composition, and ownership of land area for agriculture and dwelling units are associated with probabilities of migration for partial or entire households. In terms of household structure, households comprising unmarried children are less likely to become migrated households than extended households. Households with more educated family members are also more likely to become migrated households than households comprising members with less education (statistically, it was six or eight times higher for households with members with education). Furthermore, when a household owns more land, the smaller the probability of it becoming a migrated household. Regional differences, however, have less significance to family migration. In general, empirical phenomena on family migration in Indonesia are similar to family migration in other countries in Asia (e.g., India (Battacharya, 1985), the Philippines (Castilo, 1979), and Malaysia (Chandra, 1985)). Furthermore, this review has been supported by established theoretical frameworks, which deal with migration decision (individual and family migration).

Empirical analysis and theoretical reviews on migration studies have shown that many factors affect the probability of a household becoming either a partially or entirely migrated household. Some of these are more or less constant throughout the life of the individual or individual family members, while others are associated with stages in the life cycle of the individual as well as the household.

The processes of modernity and globalisation continue to shape the family in Indonesia. Changes in the family include smaller family sizes as well as much higher proportions of women participating in the labour force. In addition, younger Indonesians are seen to postpone marriage in that they are marrying and having families later. Increasingly personal relationships are based on strategic life planning, individualism, equality and democracy. As a result of the modernisation process and enhancement in economic opportunities along with demographic changes, the characteristics of family migration in Asia in general, and in Indonesia in particular, may also change significantly when families move from the traditional pattern to the more modern.

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References

Battacharya, B. (1985) ‘The role of family decision in internal migration: The case of India’, Journal of Development Economics, 18(1), pp. 51–66.

Castilo, G. (1979) ‘Family and household: The microworld of the Filipino’, Beyond Manila, International Development Research Centre, Ottawa, pp. 103–131.

Chandra, A. (1985) ‘The family migration process in Peninsular Malaysia’, doctoral dissertation, Department of Economics, University of North Carolina at Chapel Hill.

Corner, L and Tirtosudarmo, R. (1985) ‘The Indonesian major migration survey: Current status and preliminary results’, paper presented in International Symposium on National Migration Survey in Asia, Seoul, 17–19 April.

Geertz, H. (1961) The Javanese Family: A Study of Kinship and Socialization, Glencoe: The Free Press.

Gooszen, H. (1999) A Demographic History of the Indonesian Archipelago, 1880-1942, Leiden: KITLV Koninklijk Instituut voor Taal, Land, en Volkenkunde Press.

Hugo, G.J. (1981) ‘Village-community ties, village norms, and ethic and social networks: A review of evidence from the Third World’, in G. F. De Jong and R. W. Gardner (eds.), Migration Decision Making, New York: Pergamon Press, pp. 186–224.

Hugo, G.J. (1999) ‘Changing pattern of internal and international population mobility in Indonesia’, paper presented at the seminar The Challenges of Population Mobility in Indonesia toward Globalisation Era, Jakarta, 19 October, 1999.

Lee, E. (1966) ‘A theory of migration’, Demography, 6, pp. 47–57.

Masey, D. S. (1990) ‘Social structure, household strategies, and the cumulative causation of migration’, Population Index, 56, pp. 3–26.

Sjaastad, L. A. (1962) ‘The cost of human migration’, Journal of Political Economy, 70, pp. 80–93.

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

Spaan, E. (1999) ‘Labour circulation and socio-economic transformation: The case of East Java, Indonesia’, doctoral dissertation, Department of Spatial Sciences, University of Groningen, the Netherlands.

Stark, O. (1991) The Migration of Labour, Cambridge: Basil Blackwell.

Tirtosudarmo, R. (1997) ‘From emigratie to transmigration: Continuity and change in migration policies in Indonesia’, Population Studies and Training Centre, Brown University, PSTC Working Paper, No. 5.

Todaro, M.P. (1980) ‘International migration in developing countries: A survey’, in R.A. Easterlin (ed.), Population and Economic Change in Developing Countries, Chicago, NBER, pp. 361–402.

White, B. and Hastuti, E.L. (1980) ‘Subordinasi tersembunyi: Pengaruh pria dan wanita dalam kegiatan rumah tangga dan musyawarah di dua desa di Jawa Barat’ (‘Invisible subordination: the role of male and female in family deliberation activity, in two villages, West Java’), Studi Dimanika Pedesaan, Survey Agro Ekonomi, Bogor.

Wolpert, J. (1965) ‘Behavioural aspects of the decision to migrate’, paper of the Regional Science Association, 15, pp. 159–169.

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Chapter 4

Migration and Household Characteristics: Evidence

from China

Zhou Hao Department of Sociology

Peking University, Peiking

Abstract

Using data from the 1992 fertility sample survey conducted by the State Family Planning Commission of China (SFPC), this paper explores the impact of some household characteristics on out-migration decision-making. It does so by applying logistic regression analysis, and by controlling the effect of the variables from community and individual levels, which are based on the household in the place of original. The conclusion is that although socio-economic factors are very important for migration decision-making, the household or family remains the basic unit of daily life in China, and personal migration action is significantly affected by the characteristics of the household, in addition to personal and community factors.

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Introduction

lassic migration theories always assume that migration is an individual decision aimed at maximizing expected income. However, according to Massey, et al.

(1993), a “new economics of migration” has arisen since the 1980s “to challenge many of the assumptions and conclusions of neoclassical theory, whose key insight is that: migration decisions are not made by isolated individual actors, but by larger units of related people—typically families or households”. As such, they further propose that “families, households or other culturally defined units of production and consumption are appropriate units of analysis for migration research, not the individual”.

Though this kind of new migration theory is concerned with international migration, it can also serve as a mirror with which to explain internal migration, especially in China, with its long history and strong traditional values affected by the people’s heightened sense of family and the household.

Massey, et al.’s (1993) proposition is very suitable for application to Chinese traditional culture. After 1949, with the founding of the People’s Republic of China, private enterprises soon became disbanded under political pressure. The basic unit of society was transformed into groups of villages in the rural area, or residents in cities (Cunweihui or Juweihui). All the economic functions of households or families became considerably reduced and loosened. With the launch of the ‘Reform and Opening Up Policy’, especially the household contract responsibility system that linked remuneration with output, the inner ties between members of families and households were again strengthened as households regained their economic functions. Apart from being a vital social unit for fertility and the elderly care system, the household’s function as an economic unit of society likewise became very important for all the members of families and households.

Second, the inequilibrium model can be effectively used to explain the migration orientations based on the vastly uneven socio-economic development between provinces. However, the model cannot adequately explain the movement between those provinces with same level of socio-economic development, which has led to the use of the network theory of migration, primarily by sociologists, to explain such movements. The network theory, however, only assumes that migrants are linked by personal relationships between friends or people from the same hometown. I argue that migration orientation is not only dependent on membership in households or families, but on blood relationships, where the potential migrants’ relatives, brothers and sisters are more significant facilitators of the migration process than friends.

Third, although having a migrant brother/sister is likely to induce other members of families or households to move to the destination, such movements are mitigated by Chinese traditions, which maintain that children must support their parents when the latter are old. Indeed, the Chinese saying: “A child does not go far away if his parents are living” reflects one of the most important tenets of Chinese tradition, that is, the sense of family or household. Likewise, this idea implies that “the whole clan lives together, and

C

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moves together”. Despite the very rapid social and economic development in China in recent years, such values can still be observed in migration trends in China. For example, in a family or household with out-migrants, the elderly parents always live with another adult child so that they can be cared for. Hence, the structure of the household and the family will also influence individual migration decision-making.

According to the above three aspects, it is evident that traditional culture, especially the sense of family and household, plays a very important role in migration decision-making. It is important, therefore, to find relationships between migration and the household, which is a very important step in improving the quality of research in China. However, research based on the household is limited, with few papers having been published among the large body of the literature. Cai (1997), for example, described the effect of household and sex characteristics in migration decision-making based on the pilot survey in Jinan city. Another study by Chen and Sun (1996) used the 1% census in Wu Han in 1995 to describe the familizing characteristics of floating populations. Both papers are based on the data collected from the destination.

Why do such limitations in this kind of research exist? The reason is data paucity. From the 1987 survey on urban migrants in 74 towns and cities, to the fifth census in 2000, the relevant surveys covering migration only interviewed migrants at the destination, and did not provide the characteristics of the migrants’ households at origin. Although the place where the sample surveys were conducted can be regarded as the original and destination place at the same time, these surveys only accepted them as the latter. Even so, the questionnaire did not include information on out-migrants and their households. Hence, such research has been limited.

Hypothesis

After reviewing the literature on China, and based on the accessible data source, the hypothesis of this paper is: Besides personal characteristics and socio-economic factors, individual migration action is significantly influenced by the characteristics of household, because the family or household is the basic social and economic unit in China.

Although we have the hypothesis, it is very difficult to decide how to establish a framework of household characteristics with representative indices. In this paper, based on the data, the characteristics selected include: (1) the size of family or household (representative indices are: the number of adults in a household; if there are children in the household or not; and the number of elderly dependants in the household); (2) If parents live together (if living with mother, and if living with father); (3) Number of brothers/sisters.

Here, it should be explained that the object of this research is the household with migrant(s) at the place of origin. That means that, (1) our research does not focus on the individual, but the household. (2) The objective household must contain one or more out-migrants. (3) The household researched is residing in the place of origin. Why?

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Just as we mentioned above, the place of survey can be regarded not only as the destination, but also the place of origin. Generally, however, it is perceived as the destination, a reflection of the limitations of such research. But it is obvious that the household migrant’s life at the destination is different from that in the original place. Perhaps even the household in which migrant lives at the destination is not related to him/her through a blood relationship. Even so, if we consider the reason for migration, it is the household at origin that pushes the potential migrant to make the decision to move, or is pulled by others to do so. So, it is the household at origin that should be the object of this research.

Data Source

Fortunately, the survey conducted by the State Family Planning Commission of China in 1992 provided the basic information on out-migrants and their households. The State Family Planning Commission (SFPC) conducted a survey using the Chinese family planning management information system for the first time during September to October 1992 in 30 provinces (autonomous regions and municipalities directly under the central Government). It involved 670 counties (prefectures and cities) and 2,301 village (neighbourhood) groups. The sample size is 385,000 and the sample proportion, 0.329 per thousand.

The systematic and cluster random sampling methods are used in the survey at two stages. The first stage is to take the county level as a unit for which to conduct the survey, with the sample ratio at one-quarter. At the second stage, taking village (neighborhood) groups as unit, we conducted a random sampling among the counties, which were sampled at the first stage, the sample ratio being 1.2 per thousand. The designed fertility error is 0.7 per thousand, which has well represents that of the whole of China.

The respondents of this survey include people who have lived in sampled villages or areas the night before the survey and people whose households registered at the sampled places but who were not at home at the reference time (SFPCC, et al. 1997).

Of course, we know that the respondent is not the out-migrant himself. These questionnaires were answered by another member of the same household. So, there must be some allowance of random content error due to recalling. However, with the exception of this point, we are sure that this data is reliable for us to analyze the situation of migration and the relationship between migration and households.

Two important questions are asked: the household registration situation, and the duration of stay at the destination. According to these two questions, we can identify the out- and in-migrant, and find the relevant information on their households.

In accordance with the questionnaire, this paper selected the following indices to represent some characteristics of the household with the out-migrant: family size, whether he/she is living with his/her father (or mother), and the number of brothers or sisters he/she has. Unfortunately, the relationships within the household cannot be

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identified because the questionnaire did not include the appropriate questions. Therefore, it is not possible to judge which person in the household is likely to migrate more easily or more frequently.

Meanwhile, we selected some community level variables that may be important push factors for migration, such as the environmental characteristics, nationality, level of technology in the village, etc. At the same time, individual-level variables such as age, sex and marriage status etc. are also included in the equation.

Therefore, three levels of variables have been included in the equation: 1) community level, 2) household level, 3) individual level. The variables are as follows:

The Community Level Variables

• The environmental characteristics of the area. The plain is the reference group, and the other values are: hills, semi-mountainous area, mountain area, plateau, island, and others.

• The nationality of the community, that is, which ethnic group is dominant in this community. The Han nationality is the reference group. And the Minority Nationality residence community is valued 1.

• If the community has electricity. The reference group is the community that has electricity.

• The level of income per capita in 1991 in the township (that is, Xiang). The value as “poverty” is selected as the reference group.

• The level of income per capita in 1991 among the county (that is, Xian). The reference group value is also ‘poverty’.

Household Characteristics Variables

• Number of adults in the household

• Number of elderly dependants in the household

• If there is at least one child in the household

• If migrant had brother(s) or sister(s) when he was born; yes =1, no = 0.

• If living with father (yes=1, no=0)

• If living with mother (yes=1, no=0)

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Personal Characteristics Variables

• Age (The age group of 0-4 is selected as the reference group.)

• Sex (Male=1, Female=2)

• Education status (EDU)

Marital status (MAR, Married=1, single=0)

Table 10 shows the description of the variables listed above.

Table 10 Independent Variables Used in Analysis

Variables Measurement Mean Std. deviation A. The community level variables:

1. The landforms of community.

The plain is the reference group. The other values are: hills, semi- mountain area, mountain area,

plateau, island, and others.

1.79 1.40

2. The nationality of the community

The Han nationality is the reference group. The Minority Nationality residence community is valued 1.

1.07 .26

3. If the community have electricity.

The reference group is the community that has electricity. .87 .49

4. The level of income per capita in 1991 among the township

(that is, Xiang).(Inc1)

The value “poverty” is selected as the reference group. 2.43 1.41

5. The level of income per capita in 1991 among the county (that

is, Xian).(Inc2)

The value “poverty” is selected as the reference group. 2.56 1.47

B. Household Characteristics

Variables

1.Number of adults in a household (AND_1) The continuous variable 3.90 8.25

2.Number of old in a household (OLDN_1) The continuous variable .41 1.21

3.If having a child in a household (CHN_2) Having a child: 1; No child: 0 .7420 .4375

4.Having brothers or sisters when born Having: 1; No: 0 .6563 .4749

5.If living with father (LWF) yes=1, no=0 .4525 .4977 6.If living with mother(LWM) yes=1, no=0 .4183 .4933

C. Personal Characteristics

Variables:

1.Age Age group 0-4 is reference group. 28.52 19.51 2.Sex Male=1 Female=2 1.49 .50

3.Education status (EDU) Illiterate is reference group. 2.74 1.66 4.Marital status (MAR) , Married=1, single=0 .55 .50

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Logistic regression analysis can be used for the bivariate variable. It may be better to use the Hierarchical Model because the variables are from three different levels. But generally, the variable can be put into one equation at same time using logistic regression analysis.

Data Description

The Number of Households with Migrants

A total of 92,722 households are included in the data, among which there are about 19,925 households with migrants. That means about one fifth (21.49%) of the households contain at least one migrant. The number of households with out-migrants is 13,727, which accounts for about 14.80% of the total number of households. Meanwhile, the number of households with in-migrants is 7,060 (or 7.61%); and the number of households with in- and out-migrants is 862, which accounts for about 0.93% of the total number of households.

In the original studies, migration research had largely focused on the characteristics of individual migrant, so that it was very difficult for us to estimate how many households have migrants. Now, the basic data provided showed that at least 14.8% of all the households in China have migrants.

We now turn to the distribution of households in urban and rural areas (Table 11). This table shows that migration occurs more actively in rural than urban areas. The survey data shows that there are differences between in-migrant and out-migrant households. In the 5th row, the percentage of out-migrant households in urban areas is only about 20%. Meanwhile, about 50% of in-migrant households perceive the urban area as their place of destination. Though we did find this phenomenon based on individual analysis, this data impressed upon us that even the household migrant regards the urban area as his/her place of destination.

Table 11 Distribution of Household with Migrant in Urban and Rural Area

*：the number of household with migrant；

**：number of household with out-migrant； ***：number of household with in-migrant

Rural Urban Total migrant* 14018 5907 19925

% 70.35 29.65 100 Out** 10966 2761 13727

% 78.89 20.11 100 IN*** 3513 3547 7060

% 49.76 50.24 100

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The Average Size of Households with Migrants

The average household size is the basic index used in researching issues relevant to the family or household. Of course, it concerns the number of adults, children, and elderly dependants, which will affect the migration decision-making. Table 12 lists the average size of households with all kinds of migrants.

Table 12 Average Size of Household

Average Size Urban Rural Total

Total 3.66 4.19 4.08 No migrant 3.42 4.07 3.96

With migrant 4.14 4.69 4.53 Out 4.45 4.75 4.69 In 4.08 4.65 4.37

Both 5.65 6.00 5.84

(1) The households with the largest average size are those with out- and in-migrants at the same time. These households, whose average size is about 6 people, are largely found in the rural areas. The smallest households are those without migrants in urban areas, averaging only 3.42. (2) In general, however, the households with one more migrant (in- or out-migrant or both) are larger than those without migrants, both in rural and urban area. (3) It is evident that the average size of households in rural areas is larger than that in urban areas because of the rural higher fertility. (4) Furthermore, the average size of households with out-migrants is larger than that with in-migrants.

Based on the analysis on migrant individuals, human migration in China is caused by the pressure of over-population (Zhou, 2000). The description of the average size of households with migrants based on household analysis confirms this conclusion. The problem of overpopulation in China has been well-documented. In rural areas, the household is the basic unit, given the present policy of a system of contracted responsibility linking remuneration to output. This results in serious pressures on the natural resources, which then limits the improvement of labour productivity and reduces the land area per capita. This kind of limitation induces the out-migration of some members of the household.

The average size of household includes the number of children. At the same time, the number of children, or whether there are children or not, always influences the adult’s decision-making because the adult has an obligation to care for their child dependants. It is therefore useful to describe the relative numbers of adults and children (Table 13).

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Table 13 Average Number of Adult and Children in a Household

Adult Children Urban Rural Total Urban Rural Total

Total 2.89 3.03 3.00 0.76 1.16 1.08 No migrant 2.68 2.87 2.84 0.73 1.20 1.12

With migrant 3.31 3.71 3.59 0.83 0.98 0.93

Out 3.64 3.80 3.76 0.81 0.96 0.93 In 3.20 3.59 3.39 0.88 1.07 0.97

Both 4.50 4.87 4.70 1.15 1.13 1.14

With respect to the adult, the data shows that the average number of adults in the household with migrants (in- or out-migrants, or both) is about 3.59 and larger than that of the household without migrants (2.84). This is true for the households both in rural and urban areas.

However, there are no similar generalizations for the average number of children. Households without migrants have 1.2 children on average, comparatively larger than that of the households with migrants (0.73). If the average number of children in rural areas is compared to that in urban areas, it is interesting that, while the households without migrants in rural areas have 1.2 children, which is larger than that of households with migrants (0.98), this is the reverse in urban areas. In such areas, the average number of children in households without migrants is 0.73. This is less than that of households with migrants (0.83). The explanation for this phenomenon is that in rural areas, the child is the impeding factor for the movement of adults, primarily because they need their parents’ care, while in urban areas, the parents of the children in urban areas often move in order to find better living environment and better schools for their children. For example, the parents move from one district of the city to another where there is a better school. Children become the central motivation for urban migration movements. Therefore, the number of children in a family is also an important consideration in migration decision-making.

The Average Number of Migrants in a Household

The average number of migrants in a household is about 1.71, as shown in Table 14. This is larger than that of out- or in-migrants. It is very easy to understand that the number will increase because in- and out-migration occurs simultaneously. But if the data in Table 13 is compared to that in Table 12, we can see that the average number of in-migrants (1.68) is larger than that of out-migrants, although the average size of the household with in-migrants (4.37) is less than that with out-migrants.

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Table 14 Average Number of Migrant in a Household Average number of migrant

Urban Rural Total

Migrants 1.94

(5907)

1.61

(14018)

1.71

(19925)

Out-migrants 1.79

(2761)

1.57

(10966)

1.61

(13727)

In-migrant 1.84

（3547）

1.53

(3513)

1.68

7060

Both 3.22

(401)

2.62

(461)

2.90

(862)

It refers to the number of migrant in a household; the number in the brackets is the sample cases size.

The total number of migrants per household is larger in urban areas than that in rural areas. The relatively higher numbers of in-migrants per household in urban areas as compared to that of rural areas may be explained by the fact that the main flow of migrants is from rural to urban areas. However, it is more difficult to explain why the number of out-migrants per household in urban areas is larger than that in rural areas.

Analysis

In order to find out the effect of the household level variables on migration decisions, we have to input all variables into one equation and compare the different coefficients. All the variables are listed in Table 10. An analysis conducted prior to the running of the logistic regression model, based on univariate analysis using difference testing, shows that the personal and household characteristics are significantly different between migrants and non-migrants.

The comparative strength of the variables in this model cannot be described in the Chi-square test and other correlation analyses. Previous studies also cannot tell us about the effect of household characteristics on the probability of becoming a migrant because of the limitations of the data. Therefore, a logistic regression specification is used here. The predictor variables selected for this analysis are mentioned above. The dependant variable is the status of migration (migrant=1 while non-migrant=0). The equation is as follows:

Ln[p/(1-p)] = b0+∑ bi * xi

Where p represents the probability of being a migrant; xi represents the characteristics of household and individual; b0 and bi are the coefficients from the regression.

The variables are grouped according to three levels: community, household and the individual. According to the Logistic Regression results in Tables 15,16 and 17 almost all of the variables are significant in the equation.

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Table 10 shows the results of regression analyses on all samples. We can see that the coefficients from only three variables are larger than 0.05. These are: Inc12, Inc13 and age 30–34. The first two variables are the variables from the community level. Except for them, other variables are significant in the equation.

As for the migration tendencies of people of different nationalities in the place of residence, the minority nationality tends to favour migration compared to the Han nationality, a difference of about 37%. At same time, electricity in the township reflects the development level of the community. A community without electricity tends to be a push factor for residents to move out of the community. Interestingly, with the decrease in the development level of community, the incentive to move outside also increases. It is shown by the two variables representing the income level of the community in the township and county. The richer the individual or household, the less incentive there is for them to migrate. As for environmental variables, we find a result similar to that of the community’s economic development. The possibility of migration increases with environmental degradation.

After comparing those variables from the community level, it can be concluded that in general, if the economic development level of the place of residence is better, people in this community do not want to move. Obviously, this phenomenon mirrors the real life and thoughts of people. People who are living in richer communities are not willing to move outside. On the contrary, those living in poor communities are more prone to migrate, perhaps for economic reasons.

At the household level, the variables include six factors: the number of adults, the number of elderly dependants in a household, if there is a child in a household, and if he/she lives with mother/father, and if he/she has brother(s) or sister(s). It is evident from Table 15 that they are all significant variables in the equation. The variables have positive effects on out-migration decision-making are: the number of adults (ADN) in a household, and the number of the elderly dependants (OLDN) in a household. It was found that the effect of OLDN is stronger effect than that of the number of adults. It seems very surprising that with the increasing of the number of elderly dependants in a household, the tendency of moving out has also increased. However, it can be explained thus: the elderly in a household can take good care of one another when there are more elderly people in the household, leaving the younger generation free to pursue their interests through migration.

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Table 15 Logistic Regression Result (Total Population)

Variable B S.E. Sig R Exp(B) A. Community Level 1.Nationality .3181 .0364 .0000 .0241 1.3746 2.If having electricity .1542 .0353 .0000 .0115 1.1667 3. Income1* (“Very Poor” as the Reference)

Best -.2637 .0595 .0000 -.0117 .7682 Well above average -.0848 .0442 .0549 -.0036 .9187

Average .0560 .0386 .1463 .0009 1.0576 Poor .1686 .0412 .0000 .0107 1.1836

4.Income2** (“Very Poor” as the Reference) Best -.3528 .0653 .0000 -.0146 .7027

Well above average -.3086 .0402 .0000 -.0210 .7344 Average -.1411 .0325 .0000 -.0114 .8684

Poor .0704 .0338 .0372 .0043 1.0730 5.Landforms (Plain as the reference)

Hills .5764 .0209 .0000 .0767 1.7797 semi- mountain area .4180 .0293 .0000 .0396 1.5189

mountain area .3685 .0255 .0000 .0401 1.4456 plateau .4998 .0954 .0000 .0141 1.6484 island -1.5667 .4372 .0003 -.0092 .2087 others .8679 .1196 .0000 .0199 2.3818

B. Household Level Number of adults .0666 .0038 .0000 .0487 1.0689 Number of the old .2004 .0130 .0000 .0428 1.2219 If having a child -.3913 .0214 .0000 -.0508 .6762

If living with father -.6197 .0273 .0000 -.0632 .5381 If living with mother -.1354 .0297 .0000 -.0121 .8734

If having brother/sister in born -.1757 .0184 .0000 -.0263 .8388 C. Individual Level 1.AGE (age 0-4 as reference group)

AGE 5 - 9 -.4145 .0534 .0000 -.0213 .6607 AGE10-14 -.6299 .0576 .0000 -.0303 .5326 AGE15-19 .4292 .0491 .0000 .0241 1.5361 AGE20-24 .7479 .0502 .0000 .0414 2.1126 AGE25-29 .4553 .0549 .0000 .0228 1.5766 AGE30-34 .0079 .0611 .8966 .0000 1.0080 AGE35-39 -.2617 .0610 .0000 -.0113 .7698 AGE40-44 -.6985 .0684 .0000 -.0282 .4973 AGE45-49 -1.1636 .0819 .0000 -.0395 .3124 AGE50+ -1.0944 .0615 .0000 -.0495 .3347

2.Education (Illiterate as reference) Primary school .4332 .0301 .0000 .0400 1.5422 Middle school .7055 .0321 .0000 .0612 2.0248 High school 1.1064 .0390 .0000 .0790 3.0234

College 1.1388 .1377 .0000 .0227 3.1232 3.SEX (male=1) .0841 .0175 .0000 .0128 1.0878 4.Marriage (Married =1) -.5799 .0322 .0000 -.0501 .5599 Constant -3.3133 .0770 .0000

-2 Log Likelihood 115490.003; Goodness of Fit 313885.867 Chi-Square 12888.707 with df 38 Significance .0000 Total number of cases: 385271 *: The level of income per capita in 1991 among the township (that is, Xiang) (Income1) **: The level of income per capita in 1991 among the county (that is, Xian) (Income2)

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Table 16 Logistic Regression Result (Male)

Variable B S.E. Sig R Exp(B) A. Community Level 1.Nationality .4466 .0513 .0000 .0326 1.5630 2.If having electricity .1633 .0466 .0005 .0122 1.1774 3. Income1* (“Very Poor” as the Reference)

Best -.4032 .0844 .0000 -.0173 .6682 Well above average -.1015 .0599 .0900 -.0036 .9035

Average .0787 .0515 .1268 .0022 1.0819 Poor .1791 .0549 .0011 .0112 1.1961

4.Income2** (“Very Poor” as the Reference) Best -.3496 .0898 .0001 -.0138 .7050

Well above average -.4317 .0558 .0000 -.0289 .6494 Average -.1428 .0435 .0010 -.0113 .8670

Poor .1121 .0450 .0127 .0078 1.1186 5.Landforms (Plain as the reference)

Hills .6663 .0288 .0000 .0878 1.9471 Semi- mountain area .4154 .0406 .0000 .0385 1.5150

Mountain area .3979 .0348 .0000 .0431 1.4887 Plateau .5947 .1265 .0000 .0170 1.8126 Island -1.7726 .6741 .0085 -.0084 .1699 Others 1.0748 .1567 .0000 .0255 2.9295

B. Household Level Number of adults .0545 .0048 .0000 .0426 1.0560 Number of the old .2146 .0179 .0000 .0452 1.2393 If having a child -.2213 .0295 .0000 -.0280 .8015

If living with father -.6372 .0338 .0000 -.0715 .5288 If living with mother -.4130 .0357 .0000 -.0437 .6616

If having brother/sister in born -.1428 .0253 .0000 -.0208 .8669 C. Individual Level 1.age

AGE 5 – 9 -.3702 .0753 .0000 -.0179 .6906 AGE10-14 -.5712 .0833 .0000 -.0255 .5649 AGE15-19 .6610 .0713 .0000 .0349 1.9367 AGE20-24 1.0115 .0715 .0000 .0535 2.7496 AGE25-29 .7456 .0771 .0000 .0364 2.1077 AGE30-34 .3713 .0841 .0000 .0159 1.4497 AGE35-39 .1179 .0834 .1578 .0000 1.1251 AGE40-44 -.1908 .0901 .0343 -.0060 .8263 AGE45-49 -.7735 .1082 .0000 -.0266 .4614 AGE50+ -1.0510 .0886 .0000 -.0448 .3496

2.Education (Illiterate as reference Primary school .3427 .0462 .0000 .0277 1.4087 Middle school .5629 .0485 .0000 .0438 1.7557 High school .9449 .0551 .0000 .0649 2.5725

College 1.2076 .1609 .0000 .0280 3.3454 4.Marriage (Married =1) -.8927 .0407 .0000 -.0832 .4095 Constant -3.3343 .1042 .0000

-2 Log Likelihood 61556.543; Goodness of Fit 159383.101 Chi-Square 7609.031 with df 37 Significance .0000 Total number of cases: 196396 *: The level of income per capita in 1991 among the township (that is, Xiang) (Income1) **: The level of income per capita in 1991 among the county (that is, Xian) (Income2)

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Table 17 Logistic Regression Result (Female)

Variable B S.E. Sig R Exp(B) A. Community Level 1.Nationality .2081 .0520 .0001 .0154 1.2313 2.If having electricity .1570 .0543 .0039 .0104 1.1700 3. Income1* (“Very Poor” as the Reference)

Best -.1182 .0849 .1637 .0000 .8885 Well above average -.0602 .0659 .3610 .0000 .9416

Average .0290 .0584 .6189 .0000 1.0295 Poor .1537 .0626 .0141 .0083 1.1661

4.Income2** (Poor as the Reference) Best -.3640 .0957 .0001 -.0145 .6949

Well above average -.1812 .0588 .0021 -.0113 .8343 Average -.1382 .0492 .0050 -.0100 .8709

Poor .0204 .0516 .6925 .0000 1.0206 5.Landforms (Plain as the reference)

Hills .4762 .0308 .0000 .0633 1.6100 Semi- mountain area .4250 .0424 .0000 .0408 1.5297

Mountain area .3322 .0377 .0000 .0357 1.3941 Plateau .2954 .1470 .0445 .0059 1.3436 Island -1.3647 .5836 .0194 -.0077 .2555 Others .6195 .1874 .0009 .0123 1.8579

B. Household Level Number of adults .1179 .0073 .0000 .0659 1.1252 Number of the old .2199 .0194 .0000 .0463 1.2459 If having a child -.5625 .0314 .0000 -.0734 .5698

If living with father -.6599 .0472 .0000 -.0572 .5169 If living with mother .5975 .0573 .0000 .0425 1.8176

If having brother/sister in born -.2084 .0272 .0000 -.0310 .8119 C. Individual Level 1.age (age 0-4 as reference group)

AGE 5 – 9 -.4217 .0763 .0000 -.0220 .6559 AGE10-14 -.6655 .0813 .0000 -.0332 .5140 AGE15-19 .1788 .0696 .0102 .0088 1.1957 AGE20-24 .3737 .0740 .0000 .0199 1.4532 AGE25-29 .0615 .0815 .4504 .0000 1.0634 AGE30-34 -.5026 .0935 .0000 -.0213 .6049 AGE35-39 -.8552 .0947 .0000 -.0367 .4252 AGE40-44 -1.6193 .1142 .0000 -.0580 .1980 AGE45-49 -1.8112 .1301 .0000 -.0570 .1635 AGE50+ -1.2681 .0865 .0000 -.0600 .2814

2.Education (Illiterate as reference group) Primary school .4974 .0419 .0000 .0484 1.6444 Middle school .8104 .0454 .0000 .0733 2.2488 High school 1.2414 .0606 .0000 .0840 3.4604

College .6907 .2927 .0183 .0078 1.9951 3.Marriage (Married =1) .2676 .0578 .0000 .0181 1.3069

Constant -3.7261 .1176 .0000 -2 Log Likelihood 53123.476, Goodness of Fit 156821.795 Chi-Square 5974.321 df 37 Significance .0000 Total number of cases: 188797 *: The level of income per capita in 1991 among the township (that is, Xiang) (Income1) **: The level of income per capita in 1991 among the county (that is, Xian) (Income2)

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It is perhaps easier to understand why people are more likely to move out if there are more adults in the household. The more adults there are in a household, the more likely there will be potential migrant(s) in this household. Just as we explained above, the reproductive rate can be improved with a decrease in the number of adults. The labour in a household is sufficient to deal with the economic activities at home, which can help to ‘push’ some of the family members. This also confirms that migration in China is indeed ‘pushed’ by the pressure of overpopulation.

The other four household-level variables have negative effects on migration decision-making. First, the variable “if there is child(ren) in a household” results in a very important negative effect on migration possibility. If there is a child in a household, the possibility of moving out decreases rapidly, accounting for only about 67% of the possibility of moving out for the people who do not have one or more children in the household.

Meanwhile, the variables ‘If living with father’ and ‘If living with mother’ show same effects on the possibility of migration. Comparing to the variable ‘living with mother’, ‘living with Father’ plays a more negative role in the equation. Among the elderly, women are better able to care for themselves than are men, as women have spent much of their adult lives in the household. Therefore, if the potential migrant lives with his/her mother, he/she is more likely to be able to migrate without worries than if he/she lives with his/her father.

The variable, “if having brother(s) or sister(s)”, refers to the number of brothers or sisters an individual had when he/she was born. The Exp(B) of the variable has a negative effect on migration decision-making, showing that among the children, the old brother(s) or sister(s) always tends to migrate first, and the younger children are more likely to stay at home with their parents. This trend is also supported by the tendency for Chinese parents to give special care and attention to their youngest sons, even to the extent of spoiling him. He would also have been well looked after by his older siblings. Therefore, generally speaking, the younger the child, the less likely it is for him to migrate first. This is why the variable ‘if having brother(s) or sister(s)’ is negative in the regression equation.

Compared to the other two levels of variables, the variables from individual level affirm some of the classical theories, including that of migration selectivity. With the age range of 0–4 as the reference group, it has been found that people from the age group of between 20–24 are more likely it is to migrate than any other age group. And with the increase in age, the EXP(B) increases at first; peaks at age 20-24, and then decreases. From the ages of 30–34, age begins to have a negative effect on migration. Likewise for the effect of the education variable on migration; the higher the education level, the higher the tendency to migrate. The EXP(B) also increases with the rise in educational levels.

Marriage and the sex of the migrant are also variables in migration selectivity theories as well. As for sex, the female is reference group. It was found that males are more likely to

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migrate than females. Marriage, on the other hand, has a negative effect on migration. The EXP(B) is only 0.5599, which means that the possibility of married people migrating is only 56% of that of unmarried people.

If we just consider the different sexes, the sample of males and females are selected and logistic regressions are applied respectively. Table 16 and Table 17 show the results of the regressions. In these tables, it is evident that the effects of variables from the community level have same impacts as the results from applying regression analysis on the total population. This is the same for the variables from the household level, except the variable ‘living with mother’, for females. For males and the total population, the EXP(B) of this variable is negative, which means if he/she lives with his/her mother, he/she has a lower tendency to migrate. But for the female, that EXP(B) is positive and shows strong effects on migration decision-making, which means that living with her mother induces her to migrate.

Another difference between males and females is the effect of marriage on migration decision-making. For males, marriage means the decreasing possibility of migrate, because the EXP(B) is less than 1. On the contrary, the EXP(B) of marriage for females is positive, which means that married woman are more likely to move outside, with the possibility increasing by about 30% than that of the unmarried people. On one hand, marriage induces migration because the reason for the woman to move is marriage. Chinese society is patriarchal, and the bride is expected to move to her husband’s place of residence and become a member of his household, as well as take on his family name. On the other hand, there is an increasing trend for married couples in rural areas to hope to migrate outside again. Hence, the female is more likely than the male to migrate after marriage. The possibility decreases by about 60% for the married man, comparing to unmarried man. Therefore, marriage offers different sets of migration possibilities to males and females.

Conclusion

The description and analysis of the data above give us a picture of the effects of household characteristics on migration decision-making.

First of all, it was found that the data of the survey conducted by the State Family Planning Commission of China in 1992 provides us with information on migration trends and characteristics at origin, which is much more important for understanding migration motivations than the evidence from the place of destination. Furthermore, based on the results of logistic regression analysis, we found that after controlling the effect of the variables from community and individual levels, all of the variables from household level are significant in the regression equation, not only for the total population, but also for males and females. The results show that the family or household remains a very important factor in migration decision making. In particular, the characteristics of the household play a significant role in the migration decisions of individuals. On the other hand, with the modernization of China and the apparent weakening of the idea of the

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family or household, the family remains a very important idea, particularly for people in the rural areas.

As for the six variables from the household level, the number of adults and the number of the elderly dependants in a household have a positive effect on migration decision-making—the adults in a household are potential migrants and can become real migrants directly through their own actions. The increasing number of the elderly dependants in households, due to an ageing population, facilitates the migration of adults in the household as the mutual care and support provided between the elderly allows the younger adults to migrate. It was also found that factors such as migrants living with their fathers, and the existence of older brothers or sisters and young children can have a negative effect on migration. Fathers—especially elderly ones—tend to demand more care from their adult children that do elderly women. It is also evident that the existence of children in a household tend to hold parents back from migration because of the amount of care that children require. However, such findings are not applicable to certain segments of the population; for example, the presence of the mother has a negative impact on migration for the males and the total population, and a positive factor for the female sample.

At the same time, the marital status of the individual is an important personal characteristic, which plays different roles in migration decision-making for men and women. Men largely intend to stay in their hometowns after marriage. For women, however, marriage is the impetus to move. On the other hand, unmarried women harbour hopes of moving after marriage, together with her husband. Marriage, therefore, has different meanings for men and women. However, more detailed information on migrant couples is still required, pertaining to a few issues namely: Did the couple move together, or did the husband move first? If one of the couple migrates first, how long is the lag between the two movements? These interesting questions require further study.

Though this paper discusses the variables from three levels—community, household and individual, we are more interested in household characteristics. One limitation has been the selection of the variables listed on the questionnaire, which may not represent the household characteristics exactly. Variables such as the generational structure in the household and demographic structures were not included in this analysis because of the limited data.

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References

Cai F. (1997) ‘The role of family and gender in migration decision-making’, Population Research (in Chinese), 2, pp. 7–12.

Chen X. and Sun L. (1996) ‘Analysis of familizing migration in Wu Han’, Population Science of China, 5, pp. 44–47.

Guo Z. (1999) SPSS Applications: Statistics Program for Social Science, Beijing: People’s University of China Press.

Massey, D.S., Arango, J., Hugo, G., Kouaouci, A., Pellegrino, A. and Taylor, J.E. (1993) ‘Theories of international migration: a review and appraisal’, Population and Development Review, 19(3), pp. 431–466.

SFPCC, CDC/WHO CC (1997) 1992 National Fertility and Family Planning Survey, China: Selected Research Papers in English.

Yu Z. (1998) The Impact of Traditional Culture on Human Migration in China, a collection of papers on Migration History, Ji Nan, Shangdong: Qilu Press.

Zhou H. (2000) Migration Research in China, unpublished PhD dissertation, People’s University of China.

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Chapter 5

Some Probability Models for Estimating the Propensity of

Dependants’ Migration in India and Their Applications

S.K. Singh

International Institute for Population Sciences Mumbai, India

Abstract

Micro-level research on the migration process through probabilistic models under changing situations has played a decisive role in the development of the theory of migration in recent years. The present research article aims to explain the nature of rural out-migration through some probability models. The article is divided into two parts. In the first part, a probability model has been proposed to explain the pattern of single male migration aged fifteen years and above. The second part is developed to propose a probability model to study the pattern of total number of migrants (including wife and children). The suitability of the proposed models have also been examined with several sets of observed data. The models fit the real data satisfactorily.

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Importance of the Problem and Objectives

number of migration studies (Greenwood, 1971; Kono, et al., 1965; Raiman, 1962; Spear, 1971; Yoon, 1967) have, depending on their conceptualization process and

the scale of investigations, focused their attention on aggregate variation in the migration process in relation to various socio-economic and demographic characteristics. Undoubtedly, these studies have adopted a macro-approach by focusing on highly aggregate data, where relevant facts have been explained on the basis of net or gross migration flows. However, the findings from such studies have not been able to highlight variability in the movement process due to tremendous local and regional heterogeneity occurring in the third world countries.

Recently, micro-level migration studies through probabilistic models under changing situations have played a decisive role in explaining adequately the migration decision process and the environment in which the movement occurs (Bilsborrow, 1987; Dejong, et al., 1981). Moreover, the micro-level approach in explaining the migration phenomenon has also played a vital role in explaining behavioral parameters of the process, planning various local and regional development programmes, housing policies and in the development of the theory of migration (Singh, 1990).

It is worth mentioning that the micro-level migration studies, depending upon the need and availability of data, may be done at village, household or individual level. However, a number of studies have emphasized the need for data collection at the household-level, which may be due to its recognition by most of developing countries as the basic economic unit for integrated rural development programmes. Besides, it is a notable fact that the characteristics of a household influence the decision of an individual to move. Further, socio-economic and cultural advancement is much affected by the number of migrants and a household with at least one migrant is certainly more prone to exposure to new ideas and culture of other places than households with no migrants.

In addition, the dominance of single male migration in the existing migration pattern in India has opened up new areas for research. As the majority of migrants live away from their families, whom they may visit only once or twice in a year, they often feel insecure and ignored at the place of destination and hence may need company, intimacy and sex. To fulfill their physical and emotional needs, they create their own social networks and relationships. Such networks and relationships may often be non-familial and of short duration. These circumstances, along with their disposable wages, may expose them to different kinds of alcohol and drug abuse as well as unprotected sex with persons who have an unknown sexual history. They are also exposed to an increased use of injectable drugs by sharing needles, thus making them vulnerable to HIV infection. Besides this, they may be exposed to various other environmental risk factors, such as, the availability of recreational outlets like bars, discotheques, and easy access to commercial sex workers and pornographic materials. Also the mixing of people from different areas with unknown sexual histories, gives a realistic possibility of unsafe sex in connection with drunkenness, drug use and accordingly, a lack of self control. This may increase the risk of HIV infection. The potential role of migration in spreading HIV/AIDS gets further

A

86

heightened because of visits of migrants to their native places and return migration, as well as a high propensity of onward migration among a large number of these migrants.

The main objective of this paper is to explain the pattern of rural out-migration in India and to compute, through some probabilistic models, the propensity of dependants to migrate (family migration) along with male migrants aged 15 years and above. The models are based on simple assumptions that are easy to apply and perhaps closer to the observed phenomena. The suitability of the proposed models have also been examined with certain sets of observed data on rural out migration from Eastern Uttar Pradesh.

The Data and Study Design

The basic data utilized for illustration of the proposed models have been taken from a survey on “Migration and Development in Eastern Uttar Pradesh” conducted between April 1994 and March 1996. The survey consists of visits to all the 3,600 households included in a survey on “Rural Development and Population Growth (RDPG)—Sample Survey 1978”. This survey was conducted in the rural areas of Varanasi and its neighbouring district Azamgarh of Eastern Uttar Pradesh, India under the auspices of Centre of Population Studies, Banaras Hindu University, Varanasi. The survey was launched in three types of villages representing different levels of socio-economic development. The three groups of villages have been classified semi-urban, remote or growth centre villages. The semi-urban villages are those near Varanasi city, while remote villages are situated at a comparatively longer distance from the city. The growth center villages are those which contain new households and industries have been recently established. After grouping all the villages according to these criteria, a random selection of 8, 6 and 5 villages was made from these three types of villages respectively and numbering 3,514 households were enumerated from these 19 sample villages.

The Probability Model for Single Male Migration

The first approach of model building to describe the pattern of migration from a household was made by Singh and Yadava (1981), who showed that the observed distribution of migrants (males aged fifteen years and above) from a household can be described well by the negative binomial distribution. They applied this probability distribution to the observed number of migrants from households of different size groups taken from the Demographic survey of Varanasi (Rural) (1969–70), India, and found that it described the situation well. It is, however, important to note that the negative binomial distribution may be obtained from fundamentally different sets of assumptions. One way to obtain it is via the compound Poisson process and another is via the polya process (Chiang, 1968), where the risk parameter depends on the past history of the process. There are two very different mechanisms at the micro level, which give rise to the same behavior in the system. Therefore, the proposed model for single male migration has been derived under the following two assumptions:

87

Let the migration from a household occur in clusters, with the number of clusters varying from household to household and following a Poisson distribution; that is:

[ ] )1(,2,1,0,;!

K===−

jj

ejypjθθ

where y denotes the number of clusters from a household.

The number of migrants in clusters follows a truncated displaced geometric distribution; that is:

[ ]

qp

Nkq

pqkzp N

k

−=

=−

==−

1

)2.........(,2,1;1

1

K

Where z denotes the number of migrants to a cluster and N is the maximum number of migrants in a cluster.

Under the above two assumptions the probability model for male migrants (aged fifteen years and above) from a household can be derived as follows:

Let x be the total no. of male migrants from a household migrating to urban areas in y clusters and hence;

yzzzx +++= .........21

Where the zi are identically distributed and y is independent of the zi then

[ ] ( ) ( )

( )

( )

( ) ( )( )

[ ] [ ] θ

θ

θ

θ

θ

θ

θ

θ

θ

−

=−−−

=+++

−

=

−

−

=

−−

=++

−

=

=

====

∞=≥

−

=

−=

−−=

=+++=

===+++==

∑

∑∑

∑ ∑

∑

∑

eoypoxpforkNkj

jqqpqe

je

qqp

je

qpq

qpq

jekzzzp

jypjykzzzpkxp

j

N

k

jj

kk

kkkk

jk

jjN

jkj

jk

jN

k

N

k

kkkk

j

y

k

j

k

jy

j

j

j

&,....2,1.............................../

)3......(..........!

11

!1

1

!11

!...

/...

111

...1

1

11

211

121

21

1

21L

88

The probability distribution shown by expression (3) is known as the truncated Polya-Aeppli distribution.

Application

To test the suitability of the model presented by the expression (3) for the distribution of the number of migrants, it is applied to data collected from different types of villages as explained in the previous section. The model consists of two parameters θ and q (assuming N to be known) to be estimated from the observed distribution. These two parameters are estimated by equating zeroth cell observed probability to the theoretical zeroth cell probability and observed mean to the theoretical mean, that is:

)4....(..........1

1)(

−

−=

= −

N

N

o

qNq

pxE

eP

θ

θ

Where P0 and E (X) respectively denote the probability of the zeroth cell and mean of the observed distribution.

The values of N are assumed to be 4 for households in different types of villages in Eastern Uttar Pradesh, since the average size of a rural household in the study area was about 7.2. Therefore, the maximum average number of males per household may be about 3.7 (taking sex-ratio according to Indian Census, 1991). Thus the assumed value of N seems to be a reasonable approximation.

Observed and expected frequencies for three types of villages are given in Table 18 along with the estimated values of θ and q. Table 18 shows that the average number of clusters per household varies from 0.12 in ‘Semi-urban’ villages to 0.21 in ‘Growth Center’ and to 0.26 in ‘Remote’ villages. However, the average number of migrants (males aged fifteen years and above) per cluster is smaller (1.30) in remote villages than in growth center (1.38) and semi-urban (1.38) villages. This may be due to the fact that males from households of remote villages migrate singly in different clusters while males from growth centers and semi-urban villages, mostly educated and employed in white collar jobs, migrate in groups. A comparison of the calculated values of X2 to corresponding tabulated values at the 5 percent level of significance indicates that the values are insignificant except in the semi-urban areas. However the value of x2 for this group too is insignificant at one percent level.

89

Table 18 Distribution of Households According to Number of Migrants

(Males Aged Fifteen Years and Above in Different Types of Villages)

Number of households

Type of villages

Semi-urban Remote Growth-center

Total

Number of

Migrants

Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp.

0

1

2

3.

4+

1032

95

19

10

5

1032.0

87.0

28.8

13.2

871

176

59

18

10

871.0

174.2

60.1

20.1

8.6

972

154

47

18

11

972.0

147.8

54.0

19.4

8.8

2875

425

125

46

26

2875.0

409.7

142.6

48.9

20.8

Total 1161 1161.0 1134 1134.0 1202 1202.0 3497 3497.0

θ 0.12 0.26 0.21 0.20

∧q 0.29 0.24 0.29 0.28

2x 4.31 0.49 1.82 4.21

d.f. 1 2 2 2

N (Assumed) 4 4 4 4

Information on migration from 17 households was not available. For applying a x2 test, some last cells are used as basic input.

The model 3 is also applied to the data for different caste groups where, in order to obtain a significant number of observations in each cell, the data from all the three types of villages have been merged.

The distribution of rural out-migrants (males aged fifteen years and above) in different caste groups is given in Table 19. It is noted that the average number of clusters per household is the largest in upper caste groups (0.36) and the smallest in schedule caste groups (0.11). But where the average number of migrants per cluster is concerned, this number is larger in business (1.59) and in Muslim caste groups (1.48) than in upper caste groups (1.43) where it is the smallest in the schedule caste group (1.18). This may be due to the fact that most of the migrants from the trading communities and Muslim groups are employed in businesses and private jobs at different urban-centers. On the other-hand, the migrants from the upper caste group are usually better educated, employed mostly in white collar jobs and have migrated with spouse and children.

90

Table 19 Distribution of Households According to the Number of Migrants

(Males Aged Fifteen Years and Above) in Different Caste Groups.

Upper caste Middle caste Business

caste

Functional

caste

Schedule

caste

Muslim Number of

Migrants

Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp. Obs. Exp.

0

1

2

3

4

5

6

7

8+

339

89

30

19

5

3

0

0

1

339.0

83.9

37.2

16.1

9.8

801

118

32

9

3

2

0

0

0

801.0

115.2

34.7

14.1

121

17

10

3

2

0

0

1

0

121.0

17.5

8.6

6.9

782

112

34

8

3

2

0

0

0

782

0

110.4

34.3

14.3

650

65

10

2

2

0

0

0

0

-

-

-

-

-

182

24

9

5

2

0

0

0

0

182.0

23.9

9.9

6.2

Total 486 486.0 965 965.0 154 154.0 941 941.0 729 - 222 222.0

E^

q^

x2

d.f.

N (Assumed)

0.3602

0.32

2.29

2

4

0.1863

0.23

0.28

1

4

0.2412

0.42

0.36

1

4

0.1851

0.24

0.14

1

4

0.1147

0.15

-

-

4

0.1987

0.35

0.19

1

4

It would therefore be reasonable to say that the proposed model provides a suitable description of the situation as a first approximation. This may be taken as a useful tool in calculating the various probabilities of migrants connected with the process of migration from a household and also for predictions in a specified population. Although the proposed model describes satisfactorily the pattern of out-migration for males aged fifteen years and above on different sets of data, it is unable to explain the pattern of total migration including spouse, children and relatives (dependant migrants). A probability model for the pattern of dependant migrants with at least one male migrant (aged fifteen years and above) has been described in the next section.

A Probability Model for the Propensity of Dependant’s Migration

In the previous section, the distribution of male migrants aged fifteen years and above is considered, however it has been observed that other members of the household accompany male migrants. It is therefore important to examine the distribution of total migrants from a household.

91

Let X be a random variable denoting the number of male migrants aged fifteen years and above from a household. Then the probability function of x is given by:

[ ] )5..(..........1

1 xr qpr

rxxXP

−−+

==

101

3,2,1,0

∠∠≥

−==

por

pqx

Otherwise [ ] 0== xXP

And the probability-generating function is given by:

)6.....()1()(..)1(

)()()( .

rr

rrox

xxx

qspnFeiqsp

xpssEsP

−

−

∞

=

−=

−=

== ∑

The expression given in equation 6 is the probability function of a negative binomial distribution, where r and p are the parameters of the distribution. For a given male migrant aged fifteen years and above, let Y be the random variable denoting the number of dependant migrants i.e., wife, children, relatives and friends. Then the probability function of y is given by:

[ ]

∞≤≤≥

−=≥=

−−

y

yexyYP

y

10

)7........(..........)!1(

1//1

λ

λλ

With a probability generating function of:

)8..(....................

)!1()(

)1(

1

1

s

y

yy

se

sy

etG

−−

∞

=

−−

=

−= ∑λ

λ λ

92

The expression 7 is the probability function of the displaced Poisson distribution where, λ is the risk parameter varying from household to household.

Using the probability generating functions given in equations 6 and 8, the probability generating function of dependants on male migrants (aged fifteen years and above) from a household can be obtained as follows:

{ } { } )9.....(..........]1[)( )1( rsr seqptGF −−−−= λ Expanding expression 9 into a series in s yields the probabilities of n dependants on the male migrants aged fifteen years and above from a household. Equation 9 can be rewritten as:

{ }

+= ∑∑

∞

=

∞

=

−

Ok

ki

oi

iir

ksise

riirqptGF

!)(

!)( λλ

And in the above expression, for given values of i and n the coefficient of sn is:

)10......(..........)!(

)(! in

ieri

irqpin

iir

−+ −

− λλ

That is, P [n dependent upon i male migrants aged fifteen years and above].

)11.....(..........)!(

)(!

1

inie

irirqp

niir

−+

=−

− λλ

n 7 o

1≤ i ≤ n

The joint probability function of i male migrants and the n dependents migrant from a household, say p (i, n) is given by:

)12......(..........)!(

)(!

],[in

ieri

irqpniPin

iir

−+

=−

− λλ

n > o, 1 ≤ i ≤ n

and P[i=n=o] = pr

93

Application of the Model The proposed model includes three parameters r, p and λ which are to be estimated. Since it is difficult to estimate all the three parameters together, the procedure is being handled in two steps. In the first step, the parameters r and p are estimated by the methods suggested by Cohen (1965), based on the first two raw moments of the negative binomial distribution. Once the estimates of r and p are known, λ is estimated by the equation:

)13....(..........) 1(p

rqx λ−=

−

Where x is the sample mean of the observed data. The suitability of the model has been examined with the same set of data used in the previous section. However, of the 3,497 households, only information on 3,046 households are utilized to examine the suitability of the proposed model. There were 434 households which had a greater number of male migrants (aged fifteen years and above) than dependant migrants, and 17 households which had only female and child migrants. That is why 451 households are excluded from the illustration of the proposed model.

Initially, with the help of estimates of r and p, the expected frequencies are calculated for the distribution of households according to the number of male migrants aged fifteen years and above from a household in all the three types of villages, viz. semi-urban, remote and growth-centre. The observed and expected numbers of households are given in Table 20. It is observed that the values of X2 are insignificant for all the three sets of data under study. This shows that the negative binomial distribution satisfactorily describes the distribution of households according to the number of male migrants (aged fifteen years and above) from a household. However, these expected frequencies for the distribution of the number of households in relation to the number of male migrants (aged fifteen years and above) can be calculated with the marginal probability calculated for different numbers of male migrants (aged fifteen years and above) as given in Table 20. Nevertheless, according to the proposed condition or due to the exclusion of 451 households from the illustration of the proposed model (12), the approach remains open and the total of marginal probabilities for the distribution of male migrants (aged fifteen years and above) may not be necessarily equal to one.

94

Table 20 Distribution of Households According to the Number of Male Migrants (Aged Fifteen Years and Above) in Which the Number of Male

Migrants is Less Than or Equal to the Number of Dependent Migrants in Three Types of Villages, viz(?), Semi-urban, Remote and Growth-center.

Semi-urban Remote Growth-center Number of

Migrants Observed Expected Observed Expected Observed Expected

0

1

2

3

4

1032

37

13

5

1

1028.5

44.5

10.5

4.8

871

39

11

7

1

867.7

44.8

11.1

5.4

972

35

13

7

2

967.3

43.8

11.5

6.4

Total 1088 1088.0 929 929.0 1029 1029.0

x r^

p^

x2

d.f.

0.0754

0.1001

0.5703

2.08

1

0.0926

0.1171

0.5585

2.02

1

0.0875

0.0937

0.5171

3.04

1

Further, Model 12 is applied to the observed sets of data for the number of dependant migrants from a household with at least one male migrant (aged fifteen years and above). Using the estimated values of r and p in the equation 13, the values of λ^ are estimated to be 0.986, 1.082 and 0.789 for semi-urban, remote and growth-Centers villages respectively. With the estimates of λ, the observed and expected frequencies for the number of dependant migrants having at least one male migrant (aged fifteen years and above) are given in Table 21.

95

Table 21 Distribution of Households According to the Number of Dependent Migrants from a Household with at Least one Male Migrant Aged Fifteen Years and Above, in Three Types of Villages, viz(?), Semi-

urban, Remote and Growth-center.

Semi-urban Remote Growth-center Number of

dependents Observed Expected Observed Expected Observed Expected

0

1

2

3

4

5

6

7+

1032

16

9

11

10

6

2

2

1028.5

16.5

17.7

11.1

6.0

8.2

871

15

10

9

11

8

2

3

867.7

15.2

17.7

11.7

6.6

10.1

972

15

12

12

8

7

2

1

967.3

19.9

18.1

10.3

5.5

7.9

Total 1088 1088.0 929 929.0 1029 1029.0

x

λ

x2

d.f.

0.1498

0.986

7.31

4

0.1927

1.082

7.67

4

0.1565

0.789

5.26

4

Table 21 shows that the values of x2 statistics are insignificant for all the sets of data at a five percent level of significance. That is, the probability model proposed by the compounding binomial distribution and the displaced Poisson distribution satisfactorily describe the distribution of dependant migrants having at least one male migrant (aged fifteen years and above) from a household in all the three types of villages. The value of λ is found to be highest in remote villages and lowest in growth-centre villages, whereas for semi-urban areas, it lies in between. This shows a high risk of dependent migration with male (aged 15 years and above) migration in remote villages and lower in semi-urban and growth-Centers villages respectively.

With the help of the estimates of the parameters, various joint and marginal probabilities from the proposed model 12 are worked out and are presented in Table 22. The table presents the probabilities of n (n = 1, 2, 3, 4) dependant migrants with i (i = 1, 2, 3, 4) male migrants (aged fifteen years and above) with the condition imposed that i £ n. Table 22 shows that in the case of one male migrant (aged fifteen years and above) from a household, the probabilities are decreasing with an increase in the number of dependent migrants in semi-urban and growth-centre villages. But in remote villages, there is a different pattern. It is the highest in the case of two dependant migrants instead of one, after which it then follows a declining pattern. In the case of two male migrants (aged

96

fifteen years and above) from a household, the probability of dependants migrating first increases with an increase in the number of dependants and then decreases showing an approximate inverted U shaped pattern in the case of semi-urban and growth-centre villages, whereas, in remote villages the probability of dependants migrating is found to increase continuously with an increase in the number of dependant migrants. This pattern may be due to a higher value of the risk parameter λ in remote type villages (Table 21). A higher value of λ shows a greater heterogeneity in the propensity to move for dependant migrants. Further, from Table 22, it is observed that in the semi-urban and remote types of villages the marginal probabilities for the number of dependant migrants decreases with an increase in number of dependants after two dependants, whereas probabilities for two dependants are higher than that of one dependant migrant. But in the case of growth-centre villages, it is continuously decreasing with an increase in number of dependant migrants. This contrasting pattern can be attributed to homogeneity in the propensity to move for dependant migrants with male migrants aged fifteen years and above (Table 21).

Table 22 Joint and Marginal Probabilities of i Male Migrants Aged Fifteen Years and Above and n Dependents to be Migrated from a Household in

Three Types of Villages.

Conclusion

The probability model mentioned in the first section fitted well with the distribution of households according to number of male migrants (aged fifteen years and above) in the different sets of observed data. Moreover, the number of clusters from a household and the number of migrants from each cluster are not uniform and vary according to different characteristics accounted for. Furthermore, the compounded negative binomial and

Number of male migrants aged fifteen years and above Type of village

i/n No. of dependent migrants

0 1 2 3 4 Total

Semi-urban

0 1 2 3 4

0.94535 0.1517

0.01496 0.00737 0.00242

0.00134 0.00264 0.00260

0.00015 0.00044

0.000019

0.94535 0.01517 0.01630 0.01016 0.00548

Total 0.94535 0.03992 0.00658 0.00059 0.000019

Remote

0 1 2 3 4

0.93400 0.01637 0.01771 0.00958 0.00346

0.00137 0.00296 0.00320

0.00014 0.00047

0.000017

0.93400 0.01637 0.01908 0.01268 0.00715

Total 0.93400 0.04712 0.00753 0.00061 0.000017

Growth-center

0 1 2 3 4

0.94007 0.01932 0.01525 0.00601 0.00158

0.00232 0.00366 0.00289

0.00035 0.00084

0.00006

0.94007 0.01932 0.01757 0.01002 0.00537

Total 0.94007 0.04216 0.00887 0.00119 0.00006

97

displaced Poisson distributions (proposed in the second section) satisfactorily describe the distribution of households according to the number of dependant migrants (wife, children and relatives) given that there is at least one male migrant aged fifteen years and above from a household in the area is under study. Moreover, it provides an easy and effective methodology to calculate the propensity of the migration of dependants at the household level, with an increasing number of male migrants from rural areas. This information is vital for estimating the prospects of reducing regional heterogeneity through the process of migration and return migration and to estimate the potential spread of sexually-transmitted diseases such as AIDS due to the dominance of single male migration in the existing migratory behavior in India. This study is of significance in the sense that it provides a tool for finding the propensity of migration for dependant migrants through the joint and marginal probabilities of the proposed model, but there is a small drawback. The proposed model describes the distribution of dependant migrants given at least one male migrant (aged fifteen years and above) only when the number of dependant migrants are equal to or more than the number of male migrants (aged fifteen years and above) from a household.

98

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