00074918.2012.728652
Bulletin of Indonesian Economic Studies
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
Modelling the influence of caring for the elderly on
migration: estimates and evidence from Indonesia
Anu Rammohan & Elisabetta Magnani
To cite this article: Anu Rammohan & Elisabetta Magnani (2012) Modelling the influence
of caring for the elderly on migration: estimates and evidence from Indonesia, Bulletin of
Indonesian Economic Studies, 48:3, 399-420, DOI: 10.1080/00074918.2012.728652
To link to this article: http://dx.doi.org/10.1080/00074918.2012.728652
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Bulletin of Indonesian Economic Studies, Vol. 48, No. 3, 2012: 399–420
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MODELLING THE INFLUENCE OF
CARING FOR THE ELDERLY ON MIGRATION:
ESTIMATES AND EVIDENCE FROM INDONESIA
Anu Rammohan*
University of Western Australia
Elisabetta Magnani*
University of New South Wales
In a society where children are expected to support the elderly, the ill health of an
elderly parent is likely to inluence an individual’s propensity to migrate. Using
data from the Indonesian Family Life Survey, we examine the manner in which the
responsibility to care for an elderly parent who is in poor health affects the migration
decisions of working-age adults. Our analysis suggests that individuals will be less
likely to migrate if they have elderly parents who are in poor health. These indings
are robust to speciications using alternative measures of poor health.
Keywords: migration, caregiver, caregiving, care of the elderly, Indonesia
INTRODUCTION
In many developing countries, a lack of social safety nets means that the responsibility to care for elderly parents falls mainly on family members. The consequences of such caregiving for the migration decisions of working-age adults are
not well understood, however. The early literature on migration focused on factors inluencing the likelihood of migration, at both the individual and household
levels (see, for example, Harris and Todaro 1970; Borjas 1989). Studies by Lanzona
(1998) and Agesa (2001) showed that factors such as the scarcity of jobs in rural
areas and the higher incomes that could be earned in urban areas were important
in persuading ‘surplus’ low-skilled workers as well as ‘scarce’ educated workers
to move to the cities. Studies such as these have treated migration as an economic
decision in response to wage differentials in rural–urban settings.
In an early paper, Mincer (1978) argued that family ties may have a deterrent
effect on the decision to migrate, and since the mid-1980s, when Stark and Bloom
(1985) introduced their ‘new economics of labour migration’, the inluential role of
*
[email protected]; [email protected]. We are grateful to participants
at the Australasian Development Economics Workshop (Canberra, 2008) and the Population Association of America meetings (Detroit, 2009) for useful comments on earlier drafts
of this paper, and to Marie-Claire Robitaille for excellent research assistance. The authors
gratefully acknowledge funding from the Australian Research Council Discovery Project
grants scheme.
ISSN 0007-4918 print/ISSN 1472-7234 online/12/030399-22
http://dx.doi.org/10.1080/00074918.2012.728652
© 2012 Indonesia Project ANU
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Anu Rammohan and Elisabetta Magnani
the family in migration decisions has been studied extensively.1 A large literature
on developing countries emphasises the role of family ties in migration, showing
that patterns of resource lows from individuals in urban areas to families in rural
areas are largely consistent with strategies to diversify the household’s sources of
income and thus reduce the precariousness of rural life.2 These studies suggest
that migration decisions are made in a familial context. They typically focus on
the role of remittances from adult children living in the cities to family members
left behind in the countryside. In addition to economic support, however, elderly
family members will often require physical care, particularly if they fall ill.
In China, migration has been found to be beneicial for rural households and
migrantsending villages. These beneits can take the form of higher levels of
household income (Taylor, Rozelle and De Brauw 2003); a greater ability on the
part of households to manage risk (Giles 2006; Giles and Yoo 2007); a reduction in
rural income inequality (Benjamin, Brandt and Giles 2005: 807); and the likelihood
of higher levels of local investment in productive activities (Zhao 2002). An inability to migrate because of caregiving responsibilities can therefore be detrimental
not only to working-age adults, but to their families and communities.
In most Asian countries, social safety nets for the elderly are patchy or nonexistent, and the family is an important source of informal care. This undoubtedly
affects the ability of working-age adults to migrate, especially if the parents are
unwell. We are aware of only two studies for developing countries, however, analysing the links between the migration decision and the health of family members.
Muhidin (2006) studied the effect of migration on the health of household members remaining in the countryside, using data from the 1993 and 1997 rounds of
the Indonesian Family Life Survey (IFLS) – that is, earlier versions of the datasets
used in this paper.3 Giles and Mu (2007) examined the impact of illness among
elderly parents in China on the propensity of adult children to migrate. Both
studies found that poor health among family members was an impediment to
migration by adult children. Kreager (2006) points out, however, that the impact
of migration on support networks for the aged in Indonesia has not been analysed
systematically.
Based on data from the 2000 and 2007–08 rounds of the IFLS, this paper aims to
address the following three questions. Does the need to care for an elderly parent
affect the propensity to migrate? How does the availability of alternative sources
of care (in the Indonesian case, chiely other siblings living nearby) affect the
migration decisions of working-age adults? And what effect does the gender of
the caregiver have on the propensity to migrate? We argue that in an environment
1 While there are many studies on the network externalities from migration – that is, on
the importance of social networks and family ties in fostering migrant networks – this issue
is beyond the scope of the present study.
2 See, for example, Lucas and Stark (1985); Leinbach and Watkins (1998); Chen, Chiang
and Leung (2003); Frankenberg and Kuhn (2004); and Giles and Yoo (2007).
3 The IFLS is a longitudinal survey conducted in Indonesia by the Rand Corporation in
collaboration with local partners. The sample covers over 30,000 individuals living in 13
of Indonesia’s 27 provinces, and is representative of about 83% of the population. The latest (third and fourth) rounds of the survey were conducted in 2000 and 2007–08. For more
information, see .
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Modelling the inluence of caring for the elderly on migration
401
where adult children are required to support their parents, the ill health of an
elderly parent is likely to inluence the propensity of an individual to migrate.
Our focus on Indonesia is motivated by several factors. First, Indonesia still
has substantial rural poverty, high levels of rural–urban migration and a largely
family-based, informal system of aged care. As in the rest of Asia,4 co-residence
between elderly parents and at least one adult child is a central feature of the
familial support system in Indonesia, with social sanctions imposed on adult
children who do not care for their elderly parents (Cameron 2000). Governments
across Asia actively encourage this family-oriented support system for the elderly
(Chan 1997), with few moves made to set up universal social safety nets.
Second, families and communities are still expected to provide the bulk of social
insurance in Indonesia. Although the government invested heavily in health and
education during the 1980s and 1990s, and set up a compulsory social security
program for formal sector employees, most Indonesians still do not have access
to pensions and need to make their own provisions for retirement and old age.
The government has also instituted a wide range of programs targeting poor and
nearpoor Indonesians since the Asian inancial crisis in 1998 (Sumarto, Suryahadi and Widyanti 2004: 3–8), but very few of them speciically target the elderly.
Finally, the Indonesian population is ageing rapidly. With little in the way of
social safety nets for the elderly, this will constrain the ability of working-age
children with unwell elderly parents to migrate. The issue of population ageing
in Indonesia is compounded by poverty among the aged. The elderly have little
access to public pension programs, which are limited to employees in the public
sector, or are modest (Leechor 1996; ILO 2003: 90; Ariianto 2005).
In the next section, we describe the living arrangements of the elderly and
the patterns of migration in Indonesia. This is followed by a description of the
paper’s modelling strategy and dataset. We then present the main results from
the analysis.
BACKGROUND
In Indonesia, family sizes are shrinking, the population is ageing, and the nature
of the health problems being experienced by the elderly is changing. These demographic changes are occurring against a background of high economic growth
and a continuing exodus of rural Indonesians to the cities, or even overseas, in
search of employment and other opportunities.
Indonesia has experienced a sharp decline in fertility, with the average number
of children born per woman declining from 4.5 in 1980 to 2.2 in 2009 (OECD 2011:
19). The population aged 60 years or older is expected to rise dramatically over
the next few decades. By 2050, Indonesia is expected to have 72 million individuals aged 60 years and above, and will be one of only six countries in the world
with over 10 million people aged 80 years and above (UN 2009: 10, 24). This rapid
ageing of the population is expected to induce a drop in the share of working-age
adults as a percentage of the total population at a time when the health care needs
of elderly Indonesians will be rising.
4 See, for example, Kim and Choe (1992); Knodel, Saengtienchai and IngersollDayton
(1999); and Bongaarts and Zimmer (2001).
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Anu Rammohan and Elisabetta Magnani
According to Van Eeuwijk (2006: 61–2), Indonesia is experiencing a ‘health
transition’ in which the most prevalent diseases among the elderly are chronic,
noninfectious illnesses and injuries rather than acute infectious diseases. In contrast to the health transitions experienced in Europe and North America in the
second half of the 19th century, the changes in the health proile of the Indonesian
population are happening very quickly, and are affecting large numbers of people. The challenges of coping with an ageing population requiring long-term care
to manage chronic diseases are exacerbated by the absence of a well-functioning
public health care system (UNDP 2010).
These changes to family structure and to patterns of work and retirement pose
immediate economic challenges, particularly to the social insurance system,
which is not designed to deal with an ageing population. Currently the pension
and social insurance system covers only formal sector workers and the very poor.
The lack of social safety nets for the elderly increases the vulnerability of older
people to poor health and poverty. This raises the question of who will care for the
growing numbers of elderly as the population ages.
More than half the elderly population lives in rural areas, with Java having the
highest proportion of elderly individuals. In our dataset, the elderly generally
tend to be less well educated than younger cohorts. However, labour force participation among the elderly is reasonably high, with nearly 43% of those aged 70
years or above working up to 32 hours per week. This is consistent with the inding by Cameron and Cobb-Clarke (2008: 1,013) of high levels of labour force participation among elderly Indonesians. But it is nevertheless somewhat surprising
considering Indonesia’s large pool of surplus labour: in 2006, approximately 11%
of Indonesian workers were unemployed, and over 20% were under-employed
(Hugo 2007).
Migration trends in Indonesia
An exodus of rural dwellers to the cities has been under way in Indonesia for several decades. Between 1971 and 2000, the proportion of the population living in
urban areas rose from 17% to nearly 42%, while the urbanisation rates in the most
attractive destinations for migrants – the provinces of Jakarta, West Java, Yogyakarta, Bali and East Kalimantan – rose to 50% or higher (Rogers et al. 2004: 4).
In the Indonesian context, circular migration (merantau) is fairly common, with
seasonal migrants helping to diversify sources of rural household income by sending back remittances (Nas and Boender 2002). The children of such migrants are
usually left at home to be cared for by grandparents and other family members.
Miguel, Gertler and Levine (2006: 297) ind that an improvement in employment prospects in a ‘nearby’ district (one located within 200 kilometres of the
district capital) is associated with higher levels of outmigration, with just over
half of all migrants moving to other districts within the same province. According to Kreager (2006: 40), migration within Indonesia is dominated by individuals aged 15–29, with the majority of migrant moves made by those aged 20–24.
Although the remittances that younger family members send home do help to
diversify rural families’ sources of income and to spread risk, there are also some
drawbacks. Migration by working-age adults may increase the vulnerability of
older family members if, for example, remittances are not forthcoming, grandchildren are left in their care, assets have to be sold to fund a child’s departure, or a
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Modelling the inluence of caring for the elderly on migration
403
member of the family becomes ill and requires physical care (SchröderButterill
2004; Kreager 2006).
Increasingly, many Indonesians are choosing to go abroad to work. According to Hugo (2007), international migration by Indonesians takes two forms. The
irst is migration to more developed nations, particularly those belonging to the
Organisation for Economic Co-operation and Development (OECD). This type of
migration tends to be permanent and is dominated by skilled migrants. The second is the better-known, temporary movement of largely unskilled workers to the
Middle East and other parts of Asia. In mid-2006, the Minister of Manpower and
Transmigration reported that 2.7 million Indonesians (2.8% of the total workforce)
were working overseas with oficial permission. Of these, 83% were women, the
bulk of them working in the informal sector as housemaids, and the remainder
as daily wage labourers, caregivers to the elderly, shop assistants or waitresses
(World Bank 2006).5
MODELLING STRATEGY
Our goal is to examine the manner in which the responsibility to care for an elderly
parent who is in poor health affects the migration decisions of adult children. The
empirical strategy is derived from a simple theoretical model of household labour
supply. Assume a ixed total time endowment (T) is allocated between alternative
uses, namely participation in the labour market; caregiving (CG); and a residual,
leisure time (L). A working-age adult chooses to allocate hours optimally so as to
maximise a single-period utility function, V = V(L,CG,CO;H), where CG is the
time the adult spends caring for an elderly parent; CO is outside care, which is
either purchased in the market or provided by other family members; and the
term H indexes the need for care of the elderly.
Data on the time allocated to caregiving are largely missing from our dataset.
In the Indonesian context, where aged care is generally provided by household
members and is rarely purchased, we can reasonably assume that all care is provided by household members. Therefore, we proxy the cost of CO using a set of
householdspeciic variables, such as the number of workingage adults in the
household, the number of children aged 0–14, the number of adult siblings not
residing with the parents, and whether the house is self-owned.
Econometric speciication
We estimate the impact of parental health on the migration decision of individual
i during time t using a reduced-form binary choice model:
migi = α CGˆ i + Zi β1 + Xi β 2 + C j + ε i
(1)
where migi is a binary variable equal to one if individual i participates in the
migrant market; CGˆ i refers to the probability that individual i has an elderly parent living in close proximity who is in poor health; Zi and Xi are vectors of household and individual characteristics that affect individual i’s ability or desire to
5 There is considerable public debate in Indonesia about the numbers, rights and protection of women who leave the country to work abroad (Silvey 2006). Discussion of these
issues is beyond the scope of the current study, however.
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Anu Rammohan and Elisabetta Magnani
participate in the migrant market; and Cj captures community characteristics such
as the province of residence of the individual.
The X vector includes characteristics such as the adult child’s age, gender, educational attainment and marital status. These could be expected to inluence the
attractiveness of migration through their effect on the potential wage premium an
individual might be able to earn as a migrant compared with local employment,
and through their effect on the individual’s preference for participation in the
migrant market.
The caregiving variable, CGˆ , is hard to measure because there is no question
in the IFLS that allows us to identify a caregiver. The ideal dependent variable for
the irst equation, time allocated to care of an elderly parent, is not observed. We
do know, however, whether adult children are residing near or with their parents,
so that we can estimate the conditional probability of the event that ‘a workingage adult is living in proximity to an elderly parent who is in poor health’. Taking
this conditional probability as being equal to the expectation of the event, we
deine a respondent as being a caregiver if two conditions are met simultaneously:
the respondent has an elderly parent aged 60 years or above living in the same
province ((HH60+) = 1); and the elderly parent reports being in poor health.
Since we cannot actually observe caregiving by adult children, our caregiving
variable implicitly deines a population ‘at risk’ of being a caregiver. Assuming
that both elderly Indonesians and their children prefer to live independently if
they are able to do so, we further adopt a broad deinition of caregiving, where
the adult child lives in close proximity to the elderly parents rather than actually
residing with them. Kreager and SchröderButterill (2008: 51–2) also ind that
elderly Indonesians prefer to live on their own or with one reliable child living
close by. However, as a robustness test, we explore a more restricted deinition
where the adult child lives with the elderly parents.
A priori, it is dificult to predict the effect of an elderly parent’s ill health on a
working-age child’s decision to migrate. On the one hand, the scarcity of pensions
and aged-care services might make participation in the migrant market less attractive if the individual has an elderly parent who is not in good health – especially
if there are no siblings to act as alternative caregivers. But on the other hand, it is
possible that the presence of an elderly parent who is in poor health might actually increase the likelihood of a working-age adult migrating, to ease the strain
placed on the family inances by the parent’s illness.
The two main econometric issues that we face in this framework are the potential endogeneity of the caregiving decision, and the question of the robustness of
the variables for elderly poor health. We discuss each of these below.
Endogeneity of the caregiving decision
In estimating the impact of caregiving on the propensity to migrate, we need to
consider the possible endogeneity of the caregiving decision, because migrants
can differ fundamentally from non-migrants. The decision to act as a caregiver
for an elderly parent is likely to be endogenous if individuals with a low opportunity cost of time are more likely to provide care for the elderly. Unobservable
factors potentially correlated with observations of parental health and migration decisions are a concern, and using pre-determined household characteristics
alone will not solve this problem. Several sources of bias may be present. First,
the ability to observe participation in the migrant market may relect a potentially
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Modelling the inluence of caring for the elderly on migration
405
endogenous decision for the household. For example, the presence of an elderly
parent living nearby may facilitate an adult child’s participation in the migrant
market if the parent provides some form of informal child care. Alternatively, the
adult child may be living close to the parents because he or she lacks the initiative
or networks required to migrate. Finally, caring for an elderly parent may relect
the outcome of a bargaining process among siblings, with the individual who
chooses to care for the parents making an implicit decision not to participate in
the migrant market. In this case, caring for an elderly parent would be related to
participation in the migrant market.
We include three instruments to address the potential endogeneity of the caregiving decision, based on traditional community-level expectations about the
care of the elderly. The 2007–08 IFLS contained a unique questionnaire in which
community leaders were asked about the traditional laws and customs (adat)
pertaining to the living arrangements of the elderly. The responses relect long
standing cultural practices, and we therefore assume that these are distinct from
current labour market signals. We believe that these social norms provide us with
a completely exogenous measure of community-level expectations about elderly
caregiving and coresidence with adult children. These norms will inluence the
likelihood of a respondent being a caregiver, but are not directly related to the
respondent’s decision to migrate.
The speciic communitynorm variables that we include as instruments for the
endogeneity of caregiving are whether there is an expectation for children to take
care of elderly parents; whether the caregiving child receives a higher share of
the inheritance; and whether the caregiving child inherits the family home. The
migration participation equation is identiied by the exclusion of variables relating to community social norms.
Robustness of health measures
The next issue to consider is the robustness of our health measures. We deine
the elderly as those persons aged 60 years or over in 2000. The dataset provides
several alternative measures of selfreported health for respondents aged 50 years
and above. We focus irst on the selfassessed measures of poor health available
in the 2000 dataset for all elderly persons. The questionnaire asked respondents
whether they had suffered from a serious illness in the last four years, and to
rate their health on a simple scale. We describe an elderly parent as being in poor
health if they said they had suffered from a serious illness in the last four years
and, in addition, described themselves on the scale as being either ‘unhealthy’ or
‘somewhat unhealthy’.
We are aware of several concerns about the use of self-assessed health measures.
In particular, Lindeboom and Van Doorslaer (2004: 1,084) raise the possibility that
individuals with the same level of ‘true’ health may rate their health quite differently, because of differences in the way sub-groups interpret thresholds and categories when asked to rate their health on a simple scale.6 This could potentially
6 For example, individuals may report improved health if some time has elapsed since the
diagnosis of a serious condition. Also, individuals may rate themselves in comparison with
others in the same cultural group, peer group, educational bracket or income level, leading
to selfassessments that differ greatly from more objective measures of ‘true’ health.
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Anu Rammohan and Elisabetta Magnani
make the interpretation of self-assessed health status problematic. An additional
problem is that some individuals exhibit considerable uncertainty about their selfassessed health status, increasing the likelihood of measurement error (Crossley
and Kennedy 2000: 9). Despite these concerns, self-assessed health measures are
typically interpreted as objective measures of health, and are commonly used in
empirical research from both developed and developing countries.7
To test the robustness of our self-assessed health measure, ideally we would
like to use objective measures of poor health for the entire sample of elderly persons. Our dataset allows us to construct two such measures, but only for the sample of elderly parents who are residing with an adult child. These measures are
the elderly parent’s body mass index (BMI) and the parent’s ability to undertake
activities of daily living (ADL).
To measure BMI, we use the height and weight measures available in the 2000
dataset, using the standard formula:
BMI =
weight ( kg )
height 2 ( m 2 )
A BMI below 18.5 suggests that the person is underweight, which would increase
their susceptibility to illness. Therefore, the dummy variable takes a value of one
if the elderly person has a BMI below 18.5.
Household members were also asked a series of questions about their ability
to perform 10 common daily activities: carry a heavy load for 20 metres; walk for
5 kilometres; walk for 1 kilometre; bend over, squat and kneel; sweep the loor or
yard; draw a pail of water from a well; stand up from sitting on the loor without
help; stand up from sitting in a chair without help; dress without help; and go to
the bathroom without help. We use this information to construct a dummy variable for ADL that takes a value of one if the elderly person has dificulty carrying
out at least ive of the 10 activities, or is unable to do so.
In addition to BMI and ADL, we include a variable representing a negative
health shock as another test of the robustness of the self-assessed health measure.
This variable takes a value of one if there has been a deterioration in the selfassessed health status of the elderly parent between 2000 and 2007, that is, if the
parent reported being in good health in 2000 but in poor health in 2007.
We test the robustness of the self-assessed health measure by estimating the
models separately for the three alternative measures of elderly poor health. In
addition, we estimate a model where the caregiver lives with – rather than near
– an elderly parent whose health is self-assessed as poor. Summing up, our caregiving variable covers the following ive situations, each of which is modelled
separately.
• Speciication 1: adult child lives in the same province as an elderly parent, and
the parent is in poor health as measured by self-assessment.
• Speciication 2: adult child lives with an elderly parent, and the parent is in
poor health as measured by self-assessment.
7 Examples include Deaton and Paxson (1998) and Kennedy et al. (1998) for developed
countries, and Frankenberg and Jones (2004), Idler and Benyamini (1997) and Jylha (2009)
for developing countries.
Modelling the inluence of caring for the elderly on migration
407
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• Speciication 3: adult child lives with an elderly parent, and the parent is in
poor health as measured by BMI.
• Speciication 4: adult child lives with an elderly parent, and the parent is in
poor health as measured by ADL.
• Speciication 5: adult child lives with an elderly parent, and the parent is in
poor health as measured by a negative health shock between 2000 and 2007.
DATA
The data used in this paper come from the 2000 and 2007–08 rounds of the IFLS.
These rich and unique datasets contain detailed information on households’
demographic, labour market and economic characteristics, consumption and
health expenditures, and access to health care facilities and social safety nets. It is
ideal for our study because it provides detailed information on migrant moves,
migrant destinations and the reasons for migration.
The household is our unit of analysis, and our sample consists of individuals
who were of working age in 2000, that is, those aged 20–59 years. We restrict the
analysis to those individuals for whom we have data on all variables of interest
in both 2000 and 2007. There are 7,359 such individuals in the sample. We model
the migration decisions of working-age adults with potential caregiving responsibilities for elderly parents. We assume that the propensity to migrate will depend
on whether or not an adult is an informal caregiver to an elderly parent who is in
poor health.
The main dependent variable in the analysis is a binary variable indicating the
migration status of a working-age household member. Using data on migration in
the 2007–08 IFLS, we deine a migrant as an individual aged 20–59 years in 2000
who migrated for a period of at least six months in 2000, 2001, 2002 or 2003 for any
purpose. We impose this time restriction because we are interested in whether ill
health among the elderly at the time of the survey affected the propensity of the
respondent to migrate. Table 1 describes the variables used in the analysis for the
full sample. It shows that approximately 6.9% of respondents in the sample are
migrants.
The decision to migrate is likely to depend on a wide array of individual, household and labour market characteristics, as well as the health of older household
members. A key explanatory variable in our analysis is the caregiving variable.
As indicated previously, in addition to the model for children living near an
elderly parent who is in poor health (speciication 1), as a robustness check we
present estimates for models where the sample is restricted to working-age adults
living with the elderly parent (speciications 2–5). The proportion of respondents
who are potential caregivers differs depending on the measure of poor health
used (table 1). While 20.9% of working-age adults live in the same province as
an elderly parent whose selfreported health is poor (speciication 1), only 10.3%
actually reside with the elderly parent (speciication 2). Another 11.6% of respondents live with a parent whose health is poor as indicated by a low BMI (speciication 3), but just 4.4% with a parent who is unable to perform at least ive of the 10
activities of daily living (speciication 4). The proportion living with a parent who
has experienced a negative health shock, meanwhile, is 8.4% (speciication 5). Of
course, it is possible that the adult child does not contribute to the elderly parent’s
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Anu Rammohan and Elisabetta Magnani
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TABLE 1 Means for Full Sample (n = 7,360)
Variable
Mean
Migrant (equals 1 if respondent migrated for any reason
for at least six months in 2000, 2001, 2002 or 2003)
0.069
Individual characteristics
Age (years)
Male
Married
Education: elementary
Education: junior high
Education: senior high/tertiary
Employed in agriculture
Employed in mining/construction
Employed in manufacturing
Employed in retail/inance
Employed in social services
Employed in other sector
Knows of place to borrow
35.955
0.482
0.798
0.485
0.147
0.433
0.255
0.077
0.103
0.156
0.135
0.274
0.696
Household characteristics
No. of children aged 0–14
No. of working-age adults
House is self-owned
No. of non-co-resident siblings
Father’s education: no schooling
Father’s education: junior high
Father’s education: senior high/tertiary
Father’s education: missing
Mother’s education: no schooling
Mother’s education: junior high
Mother’s education: senior high/tertiary
1.495
2.835
0.841
3.285
0.038
0.060
0.126
0.341
0.138
0.039
0.073
Community characteristics
Rural
Aceh
North Sumatra
West Sumatra
Riau
South Sumatra
Bengkulu
Lampung
Jakarta
West Java
Central Java
Yogyakarta
East Java
Bali
0.564
0.000
0.067
0.053
0.004
0.046
0.000
0.041
0.024
0.169
0.141
0.082
0.169
0.061
Modelling the inluence of caring for the elderly on migration
409
TABLE 1 (continued) Means for Full Sample (n = 7,360)
Variable
Mean
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Community characteristics (continued)
West Nusa Tenggara
Central Kalimantan
South Kalimantan
East Kalimantan
South Sulawesi
Central Sulawesi
0.056
0.000
0.037
0.000
0.050
0.000
Parent’s residence and health status
Speciication 1: respondent lives in same
province as elderly parent, and parent is in poor
health as measured by self-assessment
0.209
Speciication 2: respondent lives with elderly parent, and
parent is in poor health as measured by self-assessment
0.103
Speciication 3: respondent lives with elderly parent, and parent
is in poor health as measured by body mass index (BMI)
0.116
Speciication 4: respondent lives with elderly parent, and parent
is in poor health as measured by activities of daily living (ADL)
0.044
Speciication 5: respondent lives with elderly parent,
and parent is in poor health as measured by a
negative health shock between 2000 and 2007
0.084
Instruments
Social norm: elderly person lives with adult child
Social norm: caregiving child inherits more
Social norm: caregiving child inherits house
0.443
0.329
0.426
care. However, given the lack of data on time allocated to caring for an elderly
parent, we are unable to use a more precise measure of elderly caregiving.
We include in our models several characteristics intended to pick up differences
between individuals who do and do not migrate. Two of the key determinants of
the decision to migrate are the expected likelihood of inding employment and
the expected wage differential should the individual choose to migrate. Both
are likely to depend on the human capital of the migrant, since individuals with
higher levels of human capital are more likely to be able to ind employment and
to earn higher wages if they choose to migrate.
Human capital is captured by variables such as the educational level and sector of employment of the respondent. Educational attainment is the highest level
of schooling a respondent has completed, namely elementary, junior high, and
senior high or higher (with no education being the reference category). These categories correspond to six, nine, and 12 or more years of schooling respectively.
Respondents with tertiary qualiications are pooled with senior high school graduates because there are so few of them in the sample. Table 1 indicates that 43.3%
of respondents fall into this category.
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410
Anu Rammohan and Elisabetta Magnani
To capture information on respondents’ sectors of employment, we construct
indicator variables for employment in agriculture, mining/construction, manufacturing, retail/inance, social services and ‘other’ sectors (with unemployment
as the base category).
The variables capturing a household’s economic status are whether the house
is self-owned and whether the respondent knows of a place to borrow money
should this be necessary. We also include a set of household-level demographic
and individual controls, including the number of working-age adults in the
household, the number of children aged 0–14 years, and the age, gender and marital status of the respondent, to control for lifecycle effects that may inluence
the decision to participate in the migrant market. Finally, we include an indicator
variable for whether the respondent has siblings living in the same province who
do not reside with the parents (non-co-resident siblings), to provide an indication
of alternative sources of care should an elderly parent fall ill. According to table 1,
the average household has 1.5 children aged 0–14, and the average respondent
has 3.3 adult siblings living in close proximity to an elderly parent whose health
is self-assessed as poor.
RESULTS
The main results of the analysis are presented in tables 2–6. Based on an instrumental variable (IV) probit estimation, table 2 presents the results for the propensity to
migrate for the full sample. In this speciication we do not include any information on parental education. A caregiver is deined here broadly as an adult child
who lives in close proximity to an elderly parent whose health is self-assessed as
poor (speciication 1). The two dependent variables are a dummy for the probability of being a caregiver and a dummy for the probability of migration. For
the full sample, we ind that caregiving responsibility for an elderly parent has a
statistically signiicant and negative association with the propensity to migrate.
Table 3 presents estimates for both the broad and narrow deinitions of care
giving, that is, where the caregiver lives near (speciication 1) or with (speciication 2) a parent whose health is self-assessed as poor. This time, we include
information on parental education. In the IV probit estimations, we ind a statistically signiicant and negative correlation between caregiving responsibilities
and the migration decisions of workingage adults, for both speciications. No
statistically signiicant relationship is observed for the caregiving variable in the
ordinary least squares (OLS) estimates, however, regardless of whether or not
information on parental information is included.
Table 4 illustrates the differential impact of caregiving on the probability of
migration for the male and female samples. The results presented in columns 1–4
illustrate that while caregiving responsibilities signiicantly reduce the probability of migration for females (for both speciications 1 and 2), for males there is a
positive association between caregiving and migration in both speciications.
How robust are the indings to changes in the deinition of poor health and
in the gender of the recipient of care? To test the robustness of the results for the
full sample, we use alternative measures of elderly poor health in the caregiving
measure used in the IV probit strategies, distinguishing between male and female
elderly parents.
Modelling the inluence of caring for the elderly on migration
411
TABLE 2 Instrumental Variable Estimation of Relationship between Caregiving and
Probability of Migrating, Excluding Information on Parental Education, Speciication 1 a
Variable
1st-stage Estimates:
Prob. of Being Caregiver
2nd-stage Estimates:
Prob. of Migrating
Coeficient
Coeficient
SE
–2.463***
(0.108)
(0.011)
(0.004)
(0.0001)
(0.013)
(0.018)
(0.020)
(0.018)
(0.016)
(0.017)
(0.016)
(0.020)
0.045
–0.009
0.0000
–0.149***
0.047
0.009
0.020
–0.015
0.085
0.020
–0.041
(0.033)
(0.012)
(0.0001)
(0.045)
(0.054)
(0.071)
(0.073)
(0.057)
(0.071)
(0.064)
(0.058)
–0.004
–0.004
0.067***
–0.032**
–0.070***
–0.016***
(0.004)
(0.004)
(0.014)
(0.013)
(0.022)
(0.002)
–0.005
–0.014
0.052
–0.050
–0.154**
–0.040***
(0.011)
(0.011)
(0.130)
(0.049)
(0.064)
(0.006)
Community characteristics
Rural
North Sumatra
West Sumatra
Riau
South Sumatra
Lampung
Jakarta
West Java
Central Java
Yogyakarta
Bali
West Nusa Tenggara
South Kalimantan
South Sulawesi
–0.020*
0.123***
0.204***
–0.073
0.092***
–0.024
0.032
0.071***
0.003
0.028
–0.005
0.122***
0.027
0.101***
(0.011)
(0.022)
(0.024)
(0.078)
(0.025)
(0.026)
(0.033)
(0.016)
(0.017)
(0.021)
(0.022)
(0.023)
(0.027)
(0.024)
–0.068*
0.314***
0.514***
–0.162
0.283***
–0.003
0.088
0.166***
–0.014
0.057
–0.040
0.260***
0.079
0.241***
(0.038)
(0.058)
(0.064)
(0.204)
(0.087)
(0.089)
(0.087)
(0.044)
(0.049)
(0.054)
(0.069)
(0.084)
(0.073)
(0.066)
Instruments
Social norm: adult child lives with parent
Social norm: caregiving child inherits more
Social norm: caregiving child inherits house
Constant
0.004
–0.010
0.011
0.273***
(0.007)
(0.011)
(0.009)
(0.077)
0.589**
(0.240)
7,359
–5,295
–0.922***
(0.008)
Caregiver
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SE
Individual characteristics
Male
Age
Age squared
Married
Education: elementary
Education: senior high/tertiary
Employed in manufacturing
Employed in retail/inance
Employed in social services
Employed in other sector
Employed in mining/construction
0.011
–0.001
0.0000
–0.048***
0.013
–0.013
–0.012
–0.020
0.014
–0.009
–0.018
Household characteristics
No. of working-age adults
No. of children aged 0–14
Self-owned house
Knows of place to borrow
Knows of place to borrow: missing
No. of non-co-resident siblings
No. of observations
Log likelihood
Chi-square probability
7,359
–5,295
a SE = standard error. *** = signiicant at 1%; ** = signiicant at 5%; * = signiicant at 10%. Dependent
variable = 1 if the respondent migrated in 2000, 2001, 2002 or 2003. Speciication 1: respondent lives in
same province as elderly parent, and parent is in poor health as measured by self-assessment.
412
Anu Rammohan and Elisabetta Magnani
TABLE 3 Instrumental Variable (IV) and Ordinary Least Squares (OLS)
Estimations of Relationship between Caregiving and Probability of Migrating,
Including Information on Parental Education, Speciications 1 and 2 a
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Variable
IV Probit
(2nd-stage Estimates:
Probability of
Migrating)
Caregiver
Individual characteristics
Male
Married
Education: elementary
Education: senior
high/tertiary
Employed in
manufacturing
Employed in
retail/inance
Employed in
social services
Employed in other sector
OLS
(Incl. Parental
Education)
(Excl. Parental
Education)
Spec. 1
(1)
Spec. 2
(2)
Spec. 1
(3)
Spec. 2
(4)
Spec. 1
(5)
Spec. 2
(6)
–2.467***
(0.110)
–2.723***
(0.974)
0.000
(0.007)
–0.001
(0.010)
–0.001
(0.007)
–0.001
(0.010)
0.043
0.112***
0.014**
0.015**
0.014**
0.014**
(0.034)
(0.041)
(0.006)
(0.006)
(0.006)
(0.006)
–0.152***
–0.326***
–0.017**
–0.017**
–0.018**
–0.018**
(0.045)
(0.057)
(0.008)
(0.008)
(0.008)
(0.008)
0.035
0.036
–0.008
–0.008
–0.008
–0.008
(0.054)
(0.093)
(0.011)
(0.011)
(0.011)
(0.011)
–0.004
0.072
0.013
0.013
0.015
0.015
(0.071)
(0.125)
(0.012)
(0.012)
(0.012)
(0.012)
0.018
0.111
0.018*
0.018*
0.018*
0.018*
(0.073)
(0.126)
(0.011)
(0.011)
(0.011)
(0.011)
–0.017
0.100
0.011
0.011
0.011
0.011
(0.057)
(0.088)
(0.009)
(0.009)
(0.009)
(0.009)
0.087
0.177*
(0.070)
(0.107)
0.034***
(0.010)
0.025***
0.034***
(0.010)
0.025***
0.035***
(0.010)
0.025***
0.035***
(0.010)
0.025***
0.019
0.149
(0.064)
(0.095)
(0.009)
(0.009)
(0.009)
(0.009)
–0.042
–0.045
–0.004
–0.004
–0.004
–0.004
(0.055)
(0.087)
(0.012)
(0.012)
(0.012)
(0.012)
Household characteristics
–0.002
No. of workingage adults
(0.011)
0.011
0.001
0.001
0.001
0.001
(0.016)
(0.002)
(0.002)
(0.002)
(0.002)
No. of children aged 0–14 –0.014
–0.039**
–0.004*
–0.004*
–0.004**
–0.004**
Employed in mining/
construction
Self-owned house
Knows of place
to borrow
Father’s education:
junior high
Father’s education:
senior high/tertiary
(0.011)
(0.016)
(0.002)
(0.002)
(0.002)
(0.002)
0.051
–0.228
–0.094***
–0.094***
–0.094***
–0.094***
(0.007)
(0.133)
(0.244)
(0.007)
(0.007)
(0.007)
–0.053
0.044
0.012*
0.012*
0.012*
0.012*
(0.049)
(0.085)
(0.007)
(0.007)
(0.007)
(0.007)
–0.000
0.171*
–0.000
(0.073)
0.178**
(0.101)
(0.014)
(0.014)
–0.025
–0.184**
–0.021*
–0.021*
(0.074)
(0.092)
(0.012)
(0.012)
Modelling the inluence of caring for the elderly on migration
413
TABLE 3 (continued) Instrumental Variable (IV) and Ordinary Least Squares
(OLS) Estimations of Relationship between Caregiving and Probability of
Migrating, Including Information on Parental Education, Speciications 1 and 2 a
Variable
IV Probit
(2nd-stage Estimates:
Probability of
Migrating)
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Spec. 1
(1)
Mother’s education:
senior high/tertiary
Mother’s education:
missing
No. of non-coresident siblings
Constant
No. of observations
Log likelihood
(Incl. Parental
Education)
(Excl. Parental
Education)
Spec. 2
(2)
Spec. 1
(3)
Spec. 2
(4)
Spec. 1
(5)
Spec. 2
(6)
–0.127
(0.095)
–0.082
(0.105)
0.001
(0.034)
–0.040***
(0.006)
0.595**
(0.237)
–0.046
(0.051)
–0.224*
(0.116)
0.114
(0.159)
–0.199***
(0.059)
–0.067**
(0.026)
0.468
(0.499)
0.001
(0.007)
–0.033*
(0.018)
0.060***
(0.016)
–0.006
(0.007)
0.001
(0.001)
0.358***
(0.045)
0.001
(0.007)
–0.033*
(0.018)
0.060***
(0.016)
–0.006
(0.007)
0.001
(0.001)
0.359***
(0.045)
0.001
(0.001)
0.356***
(0.044)
0.001
(0.001)
0.356***
(0.044)
7,359
–5,277
7,359
–2,682
9,375
–513.3
9,375
–513.3
9,375
–522.5
9,375
–522.5
Household characteristics
(continued)
–0.042
Father’s education:
missing
(0.031)
Mother’s education:
junior high
OLS
a *** = signiicant at 1%; ** = signiicant at 5%; * = signiicant at 10%. Standard errors are in brackets.
Dependent variable = 1 if the respondent migrated in 2000, 2001, 2002 or 2003. Provincial dummy variables are included in all models. Speciication 1: respondent lives in same province as elderly parent,
and parent is in poor health as measured by selfassessment. Speciication 2: respondent lives with
elderly parent, and parent is in poor health as measured by self-assessment.
Gender differences in the propensity to migrate
An important question is whether sons or daughters are more likely to support
their elderly parents. Frankenberg and Kuhn (2004: 11–13, 27) argue that Indonesia
(or at least Java) has a relatively egalitarian society in which sons and daughters
are equally likely to support their parents. However, their study is based on remittances sent home by migrant children, not physical care provided by children
who live near or with elderly parents.
Our results show that being male is signiicantly and positively associated with
the probability of migration, when a caregiver is deined as an adult child living with an elderly parent who is in poor health as measured by self-assessment
(table 3, column 2). Similarly, in the gender-disaggregated IV probit models presented in table 4, for both speciications 1 and 2 (adult children living respectively
near and with an elderly parent in self-assessed poor health), there is a statistically signiicant and positive association between the caregiver variable and the
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Variable
IV Probit
(2nd–stage Estimates: Probability of Migrating)
Speciication 1
Caregiver
Individual characteristics
Married
Education: senior high/tertiary
Employed in manufacturing
Employed in retail/inance
Employed in social services
Employed in other sectors
Employed in mining/construction
OLS
Speciication 1
Speciication 2
Male
Sample
(1)
Female
Sample
(2)
Male
Sample
(3)
Female
Sample
(4)
Male
Sample
(5)
Female
Sample
(6)
Male
Sample
(7)
Female
Sample
(8)
2.516***
(0.046)
–2.366***
(0.172)
3.470***
(0.153)
–3.340***
(0.320)
–0.004
(0.010)
0.004
(0.009)
0.008
(0.014)
–0.011
(0.013)
0.098*
(0.056)
–0.071
(0.081)
–0.018
(0.087)
0.116
(0.091)
0.051
(0.101)
–0.017
(0.084)
0.097
(0.101)
0.042
(0.059)
–0.224***
(0.070)
0.045
(0.083)
0.038
(0.107)
0.114
(0.090)
–0.049
(0.070)
0.123
(0.090)
0.063
(0.075)
–0.209
(0.254)
0.275***
(0.059)
–0.007
(0.086)
0.061
(0.093)
0.205*
(0.110)
0.099
(0.130)
0.027
(0.108)
0.114
(0.109)
0.092
(0.063)
–0.404***
(0.064)
0.036
(0.095)
0.080
(0.117)
0.125
(0.098)
0.013
(0.078)
0.102
(0.099)
0.124
(0.080)
–0.121
(0.291)
–0.008
(0.013)
–0.014
(0.020)
–0.001
(0.021)
0.030*
(0.016)
0.036**
(0.015)
0.051***
(0.014)
0.035
(0.021)
0.002
(0.014)
–0.023**
(0.010)
–0.005
(0.013)
0.024
(0.015)
0.008
(0.015)
–0.007
(0.012)
0.015
(0.014)
0.016
(0.011)
0.063
(0.040)
–0.007
(0.013)
–0.015
(0.020)
–0.001
(0.021)
0.030*
(0.016)
0.036**
(0.015)
0.051***
(0.014)
0.035
(0.021)
0.003
(0.014)
–0.024**
(0.010)
–0.005
(0.013)
0.024
(0.015)
0.008
(0.015)
–0.007
(0.012)
0.015
(0.014)
0.016
(0.011)
0.063
(0.040)
Anu Rammohan and Elisabetta Magnani
Education: elementary
Speciication 2
414
TABLE 4 Ins
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
Modelling the influence of caring for the elderly on
migration: estimates and evidence from Indonesia
Anu Rammohan & Elisabetta Magnani
To cite this article: Anu Rammohan & Elisabetta Magnani (2012) Modelling the influence
of caring for the elderly on migration: estimates and evidence from Indonesia, Bulletin of
Indonesian Economic Studies, 48:3, 399-420, DOI: 10.1080/00074918.2012.728652
To link to this article: http://dx.doi.org/10.1080/00074918.2012.728652
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Date: 18 January 2016, At: 00:28
Bulletin of Indonesian Economic Studies, Vol. 48, No. 3, 2012: 399–420
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MODELLING THE INFLUENCE OF
CARING FOR THE ELDERLY ON MIGRATION:
ESTIMATES AND EVIDENCE FROM INDONESIA
Anu Rammohan*
University of Western Australia
Elisabetta Magnani*
University of New South Wales
In a society where children are expected to support the elderly, the ill health of an
elderly parent is likely to inluence an individual’s propensity to migrate. Using
data from the Indonesian Family Life Survey, we examine the manner in which the
responsibility to care for an elderly parent who is in poor health affects the migration
decisions of working-age adults. Our analysis suggests that individuals will be less
likely to migrate if they have elderly parents who are in poor health. These indings
are robust to speciications using alternative measures of poor health.
Keywords: migration, caregiver, caregiving, care of the elderly, Indonesia
INTRODUCTION
In many developing countries, a lack of social safety nets means that the responsibility to care for elderly parents falls mainly on family members. The consequences of such caregiving for the migration decisions of working-age adults are
not well understood, however. The early literature on migration focused on factors inluencing the likelihood of migration, at both the individual and household
levels (see, for example, Harris and Todaro 1970; Borjas 1989). Studies by Lanzona
(1998) and Agesa (2001) showed that factors such as the scarcity of jobs in rural
areas and the higher incomes that could be earned in urban areas were important
in persuading ‘surplus’ low-skilled workers as well as ‘scarce’ educated workers
to move to the cities. Studies such as these have treated migration as an economic
decision in response to wage differentials in rural–urban settings.
In an early paper, Mincer (1978) argued that family ties may have a deterrent
effect on the decision to migrate, and since the mid-1980s, when Stark and Bloom
(1985) introduced their ‘new economics of labour migration’, the inluential role of
*
[email protected]; [email protected]. We are grateful to participants
at the Australasian Development Economics Workshop (Canberra, 2008) and the Population Association of America meetings (Detroit, 2009) for useful comments on earlier drafts
of this paper, and to Marie-Claire Robitaille for excellent research assistance. The authors
gratefully acknowledge funding from the Australian Research Council Discovery Project
grants scheme.
ISSN 0007-4918 print/ISSN 1472-7234 online/12/030399-22
http://dx.doi.org/10.1080/00074918.2012.728652
© 2012 Indonesia Project ANU
Downloaded by [Universitas Maritim Raja Ali Haji] at 00:28 18 January 2016
400
Anu Rammohan and Elisabetta Magnani
the family in migration decisions has been studied extensively.1 A large literature
on developing countries emphasises the role of family ties in migration, showing
that patterns of resource lows from individuals in urban areas to families in rural
areas are largely consistent with strategies to diversify the household’s sources of
income and thus reduce the precariousness of rural life.2 These studies suggest
that migration decisions are made in a familial context. They typically focus on
the role of remittances from adult children living in the cities to family members
left behind in the countryside. In addition to economic support, however, elderly
family members will often require physical care, particularly if they fall ill.
In China, migration has been found to be beneicial for rural households and
migrantsending villages. These beneits can take the form of higher levels of
household income (Taylor, Rozelle and De Brauw 2003); a greater ability on the
part of households to manage risk (Giles 2006; Giles and Yoo 2007); a reduction in
rural income inequality (Benjamin, Brandt and Giles 2005: 807); and the likelihood
of higher levels of local investment in productive activities (Zhao 2002). An inability to migrate because of caregiving responsibilities can therefore be detrimental
not only to working-age adults, but to their families and communities.
In most Asian countries, social safety nets for the elderly are patchy or nonexistent, and the family is an important source of informal care. This undoubtedly
affects the ability of working-age adults to migrate, especially if the parents are
unwell. We are aware of only two studies for developing countries, however, analysing the links between the migration decision and the health of family members.
Muhidin (2006) studied the effect of migration on the health of household members remaining in the countryside, using data from the 1993 and 1997 rounds of
the Indonesian Family Life Survey (IFLS) – that is, earlier versions of the datasets
used in this paper.3 Giles and Mu (2007) examined the impact of illness among
elderly parents in China on the propensity of adult children to migrate. Both
studies found that poor health among family members was an impediment to
migration by adult children. Kreager (2006) points out, however, that the impact
of migration on support networks for the aged in Indonesia has not been analysed
systematically.
Based on data from the 2000 and 2007–08 rounds of the IFLS, this paper aims to
address the following three questions. Does the need to care for an elderly parent
affect the propensity to migrate? How does the availability of alternative sources
of care (in the Indonesian case, chiely other siblings living nearby) affect the
migration decisions of working-age adults? And what effect does the gender of
the caregiver have on the propensity to migrate? We argue that in an environment
1 While there are many studies on the network externalities from migration – that is, on
the importance of social networks and family ties in fostering migrant networks – this issue
is beyond the scope of the present study.
2 See, for example, Lucas and Stark (1985); Leinbach and Watkins (1998); Chen, Chiang
and Leung (2003); Frankenberg and Kuhn (2004); and Giles and Yoo (2007).
3 The IFLS is a longitudinal survey conducted in Indonesia by the Rand Corporation in
collaboration with local partners. The sample covers over 30,000 individuals living in 13
of Indonesia’s 27 provinces, and is representative of about 83% of the population. The latest (third and fourth) rounds of the survey were conducted in 2000 and 2007–08. For more
information, see .
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Modelling the inluence of caring for the elderly on migration
401
where adult children are required to support their parents, the ill health of an
elderly parent is likely to inluence the propensity of an individual to migrate.
Our focus on Indonesia is motivated by several factors. First, Indonesia still
has substantial rural poverty, high levels of rural–urban migration and a largely
family-based, informal system of aged care. As in the rest of Asia,4 co-residence
between elderly parents and at least one adult child is a central feature of the
familial support system in Indonesia, with social sanctions imposed on adult
children who do not care for their elderly parents (Cameron 2000). Governments
across Asia actively encourage this family-oriented support system for the elderly
(Chan 1997), with few moves made to set up universal social safety nets.
Second, families and communities are still expected to provide the bulk of social
insurance in Indonesia. Although the government invested heavily in health and
education during the 1980s and 1990s, and set up a compulsory social security
program for formal sector employees, most Indonesians still do not have access
to pensions and need to make their own provisions for retirement and old age.
The government has also instituted a wide range of programs targeting poor and
nearpoor Indonesians since the Asian inancial crisis in 1998 (Sumarto, Suryahadi and Widyanti 2004: 3–8), but very few of them speciically target the elderly.
Finally, the Indonesian population is ageing rapidly. With little in the way of
social safety nets for the elderly, this will constrain the ability of working-age
children with unwell elderly parents to migrate. The issue of population ageing
in Indonesia is compounded by poverty among the aged. The elderly have little
access to public pension programs, which are limited to employees in the public
sector, or are modest (Leechor 1996; ILO 2003: 90; Ariianto 2005).
In the next section, we describe the living arrangements of the elderly and
the patterns of migration in Indonesia. This is followed by a description of the
paper’s modelling strategy and dataset. We then present the main results from
the analysis.
BACKGROUND
In Indonesia, family sizes are shrinking, the population is ageing, and the nature
of the health problems being experienced by the elderly is changing. These demographic changes are occurring against a background of high economic growth
and a continuing exodus of rural Indonesians to the cities, or even overseas, in
search of employment and other opportunities.
Indonesia has experienced a sharp decline in fertility, with the average number
of children born per woman declining from 4.5 in 1980 to 2.2 in 2009 (OECD 2011:
19). The population aged 60 years or older is expected to rise dramatically over
the next few decades. By 2050, Indonesia is expected to have 72 million individuals aged 60 years and above, and will be one of only six countries in the world
with over 10 million people aged 80 years and above (UN 2009: 10, 24). This rapid
ageing of the population is expected to induce a drop in the share of working-age
adults as a percentage of the total population at a time when the health care needs
of elderly Indonesians will be rising.
4 See, for example, Kim and Choe (1992); Knodel, Saengtienchai and IngersollDayton
(1999); and Bongaarts and Zimmer (2001).
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Anu Rammohan and Elisabetta Magnani
According to Van Eeuwijk (2006: 61–2), Indonesia is experiencing a ‘health
transition’ in which the most prevalent diseases among the elderly are chronic,
noninfectious illnesses and injuries rather than acute infectious diseases. In contrast to the health transitions experienced in Europe and North America in the
second half of the 19th century, the changes in the health proile of the Indonesian
population are happening very quickly, and are affecting large numbers of people. The challenges of coping with an ageing population requiring long-term care
to manage chronic diseases are exacerbated by the absence of a well-functioning
public health care system (UNDP 2010).
These changes to family structure and to patterns of work and retirement pose
immediate economic challenges, particularly to the social insurance system,
which is not designed to deal with an ageing population. Currently the pension
and social insurance system covers only formal sector workers and the very poor.
The lack of social safety nets for the elderly increases the vulnerability of older
people to poor health and poverty. This raises the question of who will care for the
growing numbers of elderly as the population ages.
More than half the elderly population lives in rural areas, with Java having the
highest proportion of elderly individuals. In our dataset, the elderly generally
tend to be less well educated than younger cohorts. However, labour force participation among the elderly is reasonably high, with nearly 43% of those aged 70
years or above working up to 32 hours per week. This is consistent with the inding by Cameron and Cobb-Clarke (2008: 1,013) of high levels of labour force participation among elderly Indonesians. But it is nevertheless somewhat surprising
considering Indonesia’s large pool of surplus labour: in 2006, approximately 11%
of Indonesian workers were unemployed, and over 20% were under-employed
(Hugo 2007).
Migration trends in Indonesia
An exodus of rural dwellers to the cities has been under way in Indonesia for several decades. Between 1971 and 2000, the proportion of the population living in
urban areas rose from 17% to nearly 42%, while the urbanisation rates in the most
attractive destinations for migrants – the provinces of Jakarta, West Java, Yogyakarta, Bali and East Kalimantan – rose to 50% or higher (Rogers et al. 2004: 4).
In the Indonesian context, circular migration (merantau) is fairly common, with
seasonal migrants helping to diversify sources of rural household income by sending back remittances (Nas and Boender 2002). The children of such migrants are
usually left at home to be cared for by grandparents and other family members.
Miguel, Gertler and Levine (2006: 297) ind that an improvement in employment prospects in a ‘nearby’ district (one located within 200 kilometres of the
district capital) is associated with higher levels of outmigration, with just over
half of all migrants moving to other districts within the same province. According to Kreager (2006: 40), migration within Indonesia is dominated by individuals aged 15–29, with the majority of migrant moves made by those aged 20–24.
Although the remittances that younger family members send home do help to
diversify rural families’ sources of income and to spread risk, there are also some
drawbacks. Migration by working-age adults may increase the vulnerability of
older family members if, for example, remittances are not forthcoming, grandchildren are left in their care, assets have to be sold to fund a child’s departure, or a
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Modelling the inluence of caring for the elderly on migration
403
member of the family becomes ill and requires physical care (SchröderButterill
2004; Kreager 2006).
Increasingly, many Indonesians are choosing to go abroad to work. According to Hugo (2007), international migration by Indonesians takes two forms. The
irst is migration to more developed nations, particularly those belonging to the
Organisation for Economic Co-operation and Development (OECD). This type of
migration tends to be permanent and is dominated by skilled migrants. The second is the better-known, temporary movement of largely unskilled workers to the
Middle East and other parts of Asia. In mid-2006, the Minister of Manpower and
Transmigration reported that 2.7 million Indonesians (2.8% of the total workforce)
were working overseas with oficial permission. Of these, 83% were women, the
bulk of them working in the informal sector as housemaids, and the remainder
as daily wage labourers, caregivers to the elderly, shop assistants or waitresses
(World Bank 2006).5
MODELLING STRATEGY
Our goal is to examine the manner in which the responsibility to care for an elderly
parent who is in poor health affects the migration decisions of adult children. The
empirical strategy is derived from a simple theoretical model of household labour
supply. Assume a ixed total time endowment (T) is allocated between alternative
uses, namely participation in the labour market; caregiving (CG); and a residual,
leisure time (L). A working-age adult chooses to allocate hours optimally so as to
maximise a single-period utility function, V = V(L,CG,CO;H), where CG is the
time the adult spends caring for an elderly parent; CO is outside care, which is
either purchased in the market or provided by other family members; and the
term H indexes the need for care of the elderly.
Data on the time allocated to caregiving are largely missing from our dataset.
In the Indonesian context, where aged care is generally provided by household
members and is rarely purchased, we can reasonably assume that all care is provided by household members. Therefore, we proxy the cost of CO using a set of
householdspeciic variables, such as the number of workingage adults in the
household, the number of children aged 0–14, the number of adult siblings not
residing with the parents, and whether the house is self-owned.
Econometric speciication
We estimate the impact of parental health on the migration decision of individual
i during time t using a reduced-form binary choice model:
migi = α CGˆ i + Zi β1 + Xi β 2 + C j + ε i
(1)
where migi is a binary variable equal to one if individual i participates in the
migrant market; CGˆ i refers to the probability that individual i has an elderly parent living in close proximity who is in poor health; Zi and Xi are vectors of household and individual characteristics that affect individual i’s ability or desire to
5 There is considerable public debate in Indonesia about the numbers, rights and protection of women who leave the country to work abroad (Silvey 2006). Discussion of these
issues is beyond the scope of the current study, however.
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Anu Rammohan and Elisabetta Magnani
participate in the migrant market; and Cj captures community characteristics such
as the province of residence of the individual.
The X vector includes characteristics such as the adult child’s age, gender, educational attainment and marital status. These could be expected to inluence the
attractiveness of migration through their effect on the potential wage premium an
individual might be able to earn as a migrant compared with local employment,
and through their effect on the individual’s preference for participation in the
migrant market.
The caregiving variable, CGˆ , is hard to measure because there is no question
in the IFLS that allows us to identify a caregiver. The ideal dependent variable for
the irst equation, time allocated to care of an elderly parent, is not observed. We
do know, however, whether adult children are residing near or with their parents,
so that we can estimate the conditional probability of the event that ‘a workingage adult is living in proximity to an elderly parent who is in poor health’. Taking
this conditional probability as being equal to the expectation of the event, we
deine a respondent as being a caregiver if two conditions are met simultaneously:
the respondent has an elderly parent aged 60 years or above living in the same
province ((HH60+) = 1); and the elderly parent reports being in poor health.
Since we cannot actually observe caregiving by adult children, our caregiving
variable implicitly deines a population ‘at risk’ of being a caregiver. Assuming
that both elderly Indonesians and their children prefer to live independently if
they are able to do so, we further adopt a broad deinition of caregiving, where
the adult child lives in close proximity to the elderly parents rather than actually
residing with them. Kreager and SchröderButterill (2008: 51–2) also ind that
elderly Indonesians prefer to live on their own or with one reliable child living
close by. However, as a robustness test, we explore a more restricted deinition
where the adult child lives with the elderly parents.
A priori, it is dificult to predict the effect of an elderly parent’s ill health on a
working-age child’s decision to migrate. On the one hand, the scarcity of pensions
and aged-care services might make participation in the migrant market less attractive if the individual has an elderly parent who is not in good health – especially
if there are no siblings to act as alternative caregivers. But on the other hand, it is
possible that the presence of an elderly parent who is in poor health might actually increase the likelihood of a working-age adult migrating, to ease the strain
placed on the family inances by the parent’s illness.
The two main econometric issues that we face in this framework are the potential endogeneity of the caregiving decision, and the question of the robustness of
the variables for elderly poor health. We discuss each of these below.
Endogeneity of the caregiving decision
In estimating the impact of caregiving on the propensity to migrate, we need to
consider the possible endogeneity of the caregiving decision, because migrants
can differ fundamentally from non-migrants. The decision to act as a caregiver
for an elderly parent is likely to be endogenous if individuals with a low opportunity cost of time are more likely to provide care for the elderly. Unobservable
factors potentially correlated with observations of parental health and migration decisions are a concern, and using pre-determined household characteristics
alone will not solve this problem. Several sources of bias may be present. First,
the ability to observe participation in the migrant market may relect a potentially
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Modelling the inluence of caring for the elderly on migration
405
endogenous decision for the household. For example, the presence of an elderly
parent living nearby may facilitate an adult child’s participation in the migrant
market if the parent provides some form of informal child care. Alternatively, the
adult child may be living close to the parents because he or she lacks the initiative
or networks required to migrate. Finally, caring for an elderly parent may relect
the outcome of a bargaining process among siblings, with the individual who
chooses to care for the parents making an implicit decision not to participate in
the migrant market. In this case, caring for an elderly parent would be related to
participation in the migrant market.
We include three instruments to address the potential endogeneity of the caregiving decision, based on traditional community-level expectations about the
care of the elderly. The 2007–08 IFLS contained a unique questionnaire in which
community leaders were asked about the traditional laws and customs (adat)
pertaining to the living arrangements of the elderly. The responses relect long
standing cultural practices, and we therefore assume that these are distinct from
current labour market signals. We believe that these social norms provide us with
a completely exogenous measure of community-level expectations about elderly
caregiving and coresidence with adult children. These norms will inluence the
likelihood of a respondent being a caregiver, but are not directly related to the
respondent’s decision to migrate.
The speciic communitynorm variables that we include as instruments for the
endogeneity of caregiving are whether there is an expectation for children to take
care of elderly parents; whether the caregiving child receives a higher share of
the inheritance; and whether the caregiving child inherits the family home. The
migration participation equation is identiied by the exclusion of variables relating to community social norms.
Robustness of health measures
The next issue to consider is the robustness of our health measures. We deine
the elderly as those persons aged 60 years or over in 2000. The dataset provides
several alternative measures of selfreported health for respondents aged 50 years
and above. We focus irst on the selfassessed measures of poor health available
in the 2000 dataset for all elderly persons. The questionnaire asked respondents
whether they had suffered from a serious illness in the last four years, and to
rate their health on a simple scale. We describe an elderly parent as being in poor
health if they said they had suffered from a serious illness in the last four years
and, in addition, described themselves on the scale as being either ‘unhealthy’ or
‘somewhat unhealthy’.
We are aware of several concerns about the use of self-assessed health measures.
In particular, Lindeboom and Van Doorslaer (2004: 1,084) raise the possibility that
individuals with the same level of ‘true’ health may rate their health quite differently, because of differences in the way sub-groups interpret thresholds and categories when asked to rate their health on a simple scale.6 This could potentially
6 For example, individuals may report improved health if some time has elapsed since the
diagnosis of a serious condition. Also, individuals may rate themselves in comparison with
others in the same cultural group, peer group, educational bracket or income level, leading
to selfassessments that differ greatly from more objective measures of ‘true’ health.
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Anu Rammohan and Elisabetta Magnani
make the interpretation of self-assessed health status problematic. An additional
problem is that some individuals exhibit considerable uncertainty about their selfassessed health status, increasing the likelihood of measurement error (Crossley
and Kennedy 2000: 9). Despite these concerns, self-assessed health measures are
typically interpreted as objective measures of health, and are commonly used in
empirical research from both developed and developing countries.7
To test the robustness of our self-assessed health measure, ideally we would
like to use objective measures of poor health for the entire sample of elderly persons. Our dataset allows us to construct two such measures, but only for the sample of elderly parents who are residing with an adult child. These measures are
the elderly parent’s body mass index (BMI) and the parent’s ability to undertake
activities of daily living (ADL).
To measure BMI, we use the height and weight measures available in the 2000
dataset, using the standard formula:
BMI =
weight ( kg )
height 2 ( m 2 )
A BMI below 18.5 suggests that the person is underweight, which would increase
their susceptibility to illness. Therefore, the dummy variable takes a value of one
if the elderly person has a BMI below 18.5.
Household members were also asked a series of questions about their ability
to perform 10 common daily activities: carry a heavy load for 20 metres; walk for
5 kilometres; walk for 1 kilometre; bend over, squat and kneel; sweep the loor or
yard; draw a pail of water from a well; stand up from sitting on the loor without
help; stand up from sitting in a chair without help; dress without help; and go to
the bathroom without help. We use this information to construct a dummy variable for ADL that takes a value of one if the elderly person has dificulty carrying
out at least ive of the 10 activities, or is unable to do so.
In addition to BMI and ADL, we include a variable representing a negative
health shock as another test of the robustness of the self-assessed health measure.
This variable takes a value of one if there has been a deterioration in the selfassessed health status of the elderly parent between 2000 and 2007, that is, if the
parent reported being in good health in 2000 but in poor health in 2007.
We test the robustness of the self-assessed health measure by estimating the
models separately for the three alternative measures of elderly poor health. In
addition, we estimate a model where the caregiver lives with – rather than near
– an elderly parent whose health is self-assessed as poor. Summing up, our caregiving variable covers the following ive situations, each of which is modelled
separately.
• Speciication 1: adult child lives in the same province as an elderly parent, and
the parent is in poor health as measured by self-assessment.
• Speciication 2: adult child lives with an elderly parent, and the parent is in
poor health as measured by self-assessment.
7 Examples include Deaton and Paxson (1998) and Kennedy et al. (1998) for developed
countries, and Frankenberg and Jones (2004), Idler and Benyamini (1997) and Jylha (2009)
for developing countries.
Modelling the inluence of caring for the elderly on migration
407
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• Speciication 3: adult child lives with an elderly parent, and the parent is in
poor health as measured by BMI.
• Speciication 4: adult child lives with an elderly parent, and the parent is in
poor health as measured by ADL.
• Speciication 5: adult child lives with an elderly parent, and the parent is in
poor health as measured by a negative health shock between 2000 and 2007.
DATA
The data used in this paper come from the 2000 and 2007–08 rounds of the IFLS.
These rich and unique datasets contain detailed information on households’
demographic, labour market and economic characteristics, consumption and
health expenditures, and access to health care facilities and social safety nets. It is
ideal for our study because it provides detailed information on migrant moves,
migrant destinations and the reasons for migration.
The household is our unit of analysis, and our sample consists of individuals
who were of working age in 2000, that is, those aged 20–59 years. We restrict the
analysis to those individuals for whom we have data on all variables of interest
in both 2000 and 2007. There are 7,359 such individuals in the sample. We model
the migration decisions of working-age adults with potential caregiving responsibilities for elderly parents. We assume that the propensity to migrate will depend
on whether or not an adult is an informal caregiver to an elderly parent who is in
poor health.
The main dependent variable in the analysis is a binary variable indicating the
migration status of a working-age household member. Using data on migration in
the 2007–08 IFLS, we deine a migrant as an individual aged 20–59 years in 2000
who migrated for a period of at least six months in 2000, 2001, 2002 or 2003 for any
purpose. We impose this time restriction because we are interested in whether ill
health among the elderly at the time of the survey affected the propensity of the
respondent to migrate. Table 1 describes the variables used in the analysis for the
full sample. It shows that approximately 6.9% of respondents in the sample are
migrants.
The decision to migrate is likely to depend on a wide array of individual, household and labour market characteristics, as well as the health of older household
members. A key explanatory variable in our analysis is the caregiving variable.
As indicated previously, in addition to the model for children living near an
elderly parent who is in poor health (speciication 1), as a robustness check we
present estimates for models where the sample is restricted to working-age adults
living with the elderly parent (speciications 2–5). The proportion of respondents
who are potential caregivers differs depending on the measure of poor health
used (table 1). While 20.9% of working-age adults live in the same province as
an elderly parent whose selfreported health is poor (speciication 1), only 10.3%
actually reside with the elderly parent (speciication 2). Another 11.6% of respondents live with a parent whose health is poor as indicated by a low BMI (speciication 3), but just 4.4% with a parent who is unable to perform at least ive of the 10
activities of daily living (speciication 4). The proportion living with a parent who
has experienced a negative health shock, meanwhile, is 8.4% (speciication 5). Of
course, it is possible that the adult child does not contribute to the elderly parent’s
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Anu Rammohan and Elisabetta Magnani
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TABLE 1 Means for Full Sample (n = 7,360)
Variable
Mean
Migrant (equals 1 if respondent migrated for any reason
for at least six months in 2000, 2001, 2002 or 2003)
0.069
Individual characteristics
Age (years)
Male
Married
Education: elementary
Education: junior high
Education: senior high/tertiary
Employed in agriculture
Employed in mining/construction
Employed in manufacturing
Employed in retail/inance
Employed in social services
Employed in other sector
Knows of place to borrow
35.955
0.482
0.798
0.485
0.147
0.433
0.255
0.077
0.103
0.156
0.135
0.274
0.696
Household characteristics
No. of children aged 0–14
No. of working-age adults
House is self-owned
No. of non-co-resident siblings
Father’s education: no schooling
Father’s education: junior high
Father’s education: senior high/tertiary
Father’s education: missing
Mother’s education: no schooling
Mother’s education: junior high
Mother’s education: senior high/tertiary
1.495
2.835
0.841
3.285
0.038
0.060
0.126
0.341
0.138
0.039
0.073
Community characteristics
Rural
Aceh
North Sumatra
West Sumatra
Riau
South Sumatra
Bengkulu
Lampung
Jakarta
West Java
Central Java
Yogyakarta
East Java
Bali
0.564
0.000
0.067
0.053
0.004
0.046
0.000
0.041
0.024
0.169
0.141
0.082
0.169
0.061
Modelling the inluence of caring for the elderly on migration
409
TABLE 1 (continued) Means for Full Sample (n = 7,360)
Variable
Mean
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Community characteristics (continued)
West Nusa Tenggara
Central Kalimantan
South Kalimantan
East Kalimantan
South Sulawesi
Central Sulawesi
0.056
0.000
0.037
0.000
0.050
0.000
Parent’s residence and health status
Speciication 1: respondent lives in same
province as elderly parent, and parent is in poor
health as measured by self-assessment
0.209
Speciication 2: respondent lives with elderly parent, and
parent is in poor health as measured by self-assessment
0.103
Speciication 3: respondent lives with elderly parent, and parent
is in poor health as measured by body mass index (BMI)
0.116
Speciication 4: respondent lives with elderly parent, and parent
is in poor health as measured by activities of daily living (ADL)
0.044
Speciication 5: respondent lives with elderly parent,
and parent is in poor health as measured by a
negative health shock between 2000 and 2007
0.084
Instruments
Social norm: elderly person lives with adult child
Social norm: caregiving child inherits more
Social norm: caregiving child inherits house
0.443
0.329
0.426
care. However, given the lack of data on time allocated to caring for an elderly
parent, we are unable to use a more precise measure of elderly caregiving.
We include in our models several characteristics intended to pick up differences
between individuals who do and do not migrate. Two of the key determinants of
the decision to migrate are the expected likelihood of inding employment and
the expected wage differential should the individual choose to migrate. Both
are likely to depend on the human capital of the migrant, since individuals with
higher levels of human capital are more likely to be able to ind employment and
to earn higher wages if they choose to migrate.
Human capital is captured by variables such as the educational level and sector of employment of the respondent. Educational attainment is the highest level
of schooling a respondent has completed, namely elementary, junior high, and
senior high or higher (with no education being the reference category). These categories correspond to six, nine, and 12 or more years of schooling respectively.
Respondents with tertiary qualiications are pooled with senior high school graduates because there are so few of them in the sample. Table 1 indicates that 43.3%
of respondents fall into this category.
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Anu Rammohan and Elisabetta Magnani
To capture information on respondents’ sectors of employment, we construct
indicator variables for employment in agriculture, mining/construction, manufacturing, retail/inance, social services and ‘other’ sectors (with unemployment
as the base category).
The variables capturing a household’s economic status are whether the house
is self-owned and whether the respondent knows of a place to borrow money
should this be necessary. We also include a set of household-level demographic
and individual controls, including the number of working-age adults in the
household, the number of children aged 0–14 years, and the age, gender and marital status of the respondent, to control for lifecycle effects that may inluence
the decision to participate in the migrant market. Finally, we include an indicator
variable for whether the respondent has siblings living in the same province who
do not reside with the parents (non-co-resident siblings), to provide an indication
of alternative sources of care should an elderly parent fall ill. According to table 1,
the average household has 1.5 children aged 0–14, and the average respondent
has 3.3 adult siblings living in close proximity to an elderly parent whose health
is self-assessed as poor.
RESULTS
The main results of the analysis are presented in tables 2–6. Based on an instrumental variable (IV) probit estimation, table 2 presents the results for the propensity to
migrate for the full sample. In this speciication we do not include any information on parental education. A caregiver is deined here broadly as an adult child
who lives in close proximity to an elderly parent whose health is self-assessed as
poor (speciication 1). The two dependent variables are a dummy for the probability of being a caregiver and a dummy for the probability of migration. For
the full sample, we ind that caregiving responsibility for an elderly parent has a
statistically signiicant and negative association with the propensity to migrate.
Table 3 presents estimates for both the broad and narrow deinitions of care
giving, that is, where the caregiver lives near (speciication 1) or with (speciication 2) a parent whose health is self-assessed as poor. This time, we include
information on parental education. In the IV probit estimations, we ind a statistically signiicant and negative correlation between caregiving responsibilities
and the migration decisions of workingage adults, for both speciications. No
statistically signiicant relationship is observed for the caregiving variable in the
ordinary least squares (OLS) estimates, however, regardless of whether or not
information on parental information is included.
Table 4 illustrates the differential impact of caregiving on the probability of
migration for the male and female samples. The results presented in columns 1–4
illustrate that while caregiving responsibilities signiicantly reduce the probability of migration for females (for both speciications 1 and 2), for males there is a
positive association between caregiving and migration in both speciications.
How robust are the indings to changes in the deinition of poor health and
in the gender of the recipient of care? To test the robustness of the results for the
full sample, we use alternative measures of elderly poor health in the caregiving
measure used in the IV probit strategies, distinguishing between male and female
elderly parents.
Modelling the inluence of caring for the elderly on migration
411
TABLE 2 Instrumental Variable Estimation of Relationship between Caregiving and
Probability of Migrating, Excluding Information on Parental Education, Speciication 1 a
Variable
1st-stage Estimates:
Prob. of Being Caregiver
2nd-stage Estimates:
Prob. of Migrating
Coeficient
Coeficient
SE
–2.463***
(0.108)
(0.011)
(0.004)
(0.0001)
(0.013)
(0.018)
(0.020)
(0.018)
(0.016)
(0.017)
(0.016)
(0.020)
0.045
–0.009
0.0000
–0.149***
0.047
0.009
0.020
–0.015
0.085
0.020
–0.041
(0.033)
(0.012)
(0.0001)
(0.045)
(0.054)
(0.071)
(0.073)
(0.057)
(0.071)
(0.064)
(0.058)
–0.004
–0.004
0.067***
–0.032**
–0.070***
–0.016***
(0.004)
(0.004)
(0.014)
(0.013)
(0.022)
(0.002)
–0.005
–0.014
0.052
–0.050
–0.154**
–0.040***
(0.011)
(0.011)
(0.130)
(0.049)
(0.064)
(0.006)
Community characteristics
Rural
North Sumatra
West Sumatra
Riau
South Sumatra
Lampung
Jakarta
West Java
Central Java
Yogyakarta
Bali
West Nusa Tenggara
South Kalimantan
South Sulawesi
–0.020*
0.123***
0.204***
–0.073
0.092***
–0.024
0.032
0.071***
0.003
0.028
–0.005
0.122***
0.027
0.101***
(0.011)
(0.022)
(0.024)
(0.078)
(0.025)
(0.026)
(0.033)
(0.016)
(0.017)
(0.021)
(0.022)
(0.023)
(0.027)
(0.024)
–0.068*
0.314***
0.514***
–0.162
0.283***
–0.003
0.088
0.166***
–0.014
0.057
–0.040
0.260***
0.079
0.241***
(0.038)
(0.058)
(0.064)
(0.204)
(0.087)
(0.089)
(0.087)
(0.044)
(0.049)
(0.054)
(0.069)
(0.084)
(0.073)
(0.066)
Instruments
Social norm: adult child lives with parent
Social norm: caregiving child inherits more
Social norm: caregiving child inherits house
Constant
0.004
–0.010
0.011
0.273***
(0.007)
(0.011)
(0.009)
(0.077)
0.589**
(0.240)
7,359
–5,295
–0.922***
(0.008)
Caregiver
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SE
Individual characteristics
Male
Age
Age squared
Married
Education: elementary
Education: senior high/tertiary
Employed in manufacturing
Employed in retail/inance
Employed in social services
Employed in other sector
Employed in mining/construction
0.011
–0.001
0.0000
–0.048***
0.013
–0.013
–0.012
–0.020
0.014
–0.009
–0.018
Household characteristics
No. of working-age adults
No. of children aged 0–14
Self-owned house
Knows of place to borrow
Knows of place to borrow: missing
No. of non-co-resident siblings
No. of observations
Log likelihood
Chi-square probability
7,359
–5,295
a SE = standard error. *** = signiicant at 1%; ** = signiicant at 5%; * = signiicant at 10%. Dependent
variable = 1 if the respondent migrated in 2000, 2001, 2002 or 2003. Speciication 1: respondent lives in
same province as elderly parent, and parent is in poor health as measured by self-assessment.
412
Anu Rammohan and Elisabetta Magnani
TABLE 3 Instrumental Variable (IV) and Ordinary Least Squares (OLS)
Estimations of Relationship between Caregiving and Probability of Migrating,
Including Information on Parental Education, Speciications 1 and 2 a
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Variable
IV Probit
(2nd-stage Estimates:
Probability of
Migrating)
Caregiver
Individual characteristics
Male
Married
Education: elementary
Education: senior
high/tertiary
Employed in
manufacturing
Employed in
retail/inance
Employed in
social services
Employed in other sector
OLS
(Incl. Parental
Education)
(Excl. Parental
Education)
Spec. 1
(1)
Spec. 2
(2)
Spec. 1
(3)
Spec. 2
(4)
Spec. 1
(5)
Spec. 2
(6)
–2.467***
(0.110)
–2.723***
(0.974)
0.000
(0.007)
–0.001
(0.010)
–0.001
(0.007)
–0.001
(0.010)
0.043
0.112***
0.014**
0.015**
0.014**
0.014**
(0.034)
(0.041)
(0.006)
(0.006)
(0.006)
(0.006)
–0.152***
–0.326***
–0.017**
–0.017**
–0.018**
–0.018**
(0.045)
(0.057)
(0.008)
(0.008)
(0.008)
(0.008)
0.035
0.036
–0.008
–0.008
–0.008
–0.008
(0.054)
(0.093)
(0.011)
(0.011)
(0.011)
(0.011)
–0.004
0.072
0.013
0.013
0.015
0.015
(0.071)
(0.125)
(0.012)
(0.012)
(0.012)
(0.012)
0.018
0.111
0.018*
0.018*
0.018*
0.018*
(0.073)
(0.126)
(0.011)
(0.011)
(0.011)
(0.011)
–0.017
0.100
0.011
0.011
0.011
0.011
(0.057)
(0.088)
(0.009)
(0.009)
(0.009)
(0.009)
0.087
0.177*
(0.070)
(0.107)
0.034***
(0.010)
0.025***
0.034***
(0.010)
0.025***
0.035***
(0.010)
0.025***
0.035***
(0.010)
0.025***
0.019
0.149
(0.064)
(0.095)
(0.009)
(0.009)
(0.009)
(0.009)
–0.042
–0.045
–0.004
–0.004
–0.004
–0.004
(0.055)
(0.087)
(0.012)
(0.012)
(0.012)
(0.012)
Household characteristics
–0.002
No. of workingage adults
(0.011)
0.011
0.001
0.001
0.001
0.001
(0.016)
(0.002)
(0.002)
(0.002)
(0.002)
No. of children aged 0–14 –0.014
–0.039**
–0.004*
–0.004*
–0.004**
–0.004**
Employed in mining/
construction
Self-owned house
Knows of place
to borrow
Father’s education:
junior high
Father’s education:
senior high/tertiary
(0.011)
(0.016)
(0.002)
(0.002)
(0.002)
(0.002)
0.051
–0.228
–0.094***
–0.094***
–0.094***
–0.094***
(0.007)
(0.133)
(0.244)
(0.007)
(0.007)
(0.007)
–0.053
0.044
0.012*
0.012*
0.012*
0.012*
(0.049)
(0.085)
(0.007)
(0.007)
(0.007)
(0.007)
–0.000
0.171*
–0.000
(0.073)
0.178**
(0.101)
(0.014)
(0.014)
–0.025
–0.184**
–0.021*
–0.021*
(0.074)
(0.092)
(0.012)
(0.012)
Modelling the inluence of caring for the elderly on migration
413
TABLE 3 (continued) Instrumental Variable (IV) and Ordinary Least Squares
(OLS) Estimations of Relationship between Caregiving and Probability of
Migrating, Including Information on Parental Education, Speciications 1 and 2 a
Variable
IV Probit
(2nd-stage Estimates:
Probability of
Migrating)
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Spec. 1
(1)
Mother’s education:
senior high/tertiary
Mother’s education:
missing
No. of non-coresident siblings
Constant
No. of observations
Log likelihood
(Incl. Parental
Education)
(Excl. Parental
Education)
Spec. 2
(2)
Spec. 1
(3)
Spec. 2
(4)
Spec. 1
(5)
Spec. 2
(6)
–0.127
(0.095)
–0.082
(0.105)
0.001
(0.034)
–0.040***
(0.006)
0.595**
(0.237)
–0.046
(0.051)
–0.224*
(0.116)
0.114
(0.159)
–0.199***
(0.059)
–0.067**
(0.026)
0.468
(0.499)
0.001
(0.007)
–0.033*
(0.018)
0.060***
(0.016)
–0.006
(0.007)
0.001
(0.001)
0.358***
(0.045)
0.001
(0.007)
–0.033*
(0.018)
0.060***
(0.016)
–0.006
(0.007)
0.001
(0.001)
0.359***
(0.045)
0.001
(0.001)
0.356***
(0.044)
0.001
(0.001)
0.356***
(0.044)
7,359
–5,277
7,359
–2,682
9,375
–513.3
9,375
–513.3
9,375
–522.5
9,375
–522.5
Household characteristics
(continued)
–0.042
Father’s education:
missing
(0.031)
Mother’s education:
junior high
OLS
a *** = signiicant at 1%; ** = signiicant at 5%; * = signiicant at 10%. Standard errors are in brackets.
Dependent variable = 1 if the respondent migrated in 2000, 2001, 2002 or 2003. Provincial dummy variables are included in all models. Speciication 1: respondent lives in same province as elderly parent,
and parent is in poor health as measured by selfassessment. Speciication 2: respondent lives with
elderly parent, and parent is in poor health as measured by self-assessment.
Gender differences in the propensity to migrate
An important question is whether sons or daughters are more likely to support
their elderly parents. Frankenberg and Kuhn (2004: 11–13, 27) argue that Indonesia
(or at least Java) has a relatively egalitarian society in which sons and daughters
are equally likely to support their parents. However, their study is based on remittances sent home by migrant children, not physical care provided by children
who live near or with elderly parents.
Our results show that being male is signiicantly and positively associated with
the probability of migration, when a caregiver is deined as an adult child living with an elderly parent who is in poor health as measured by self-assessment
(table 3, column 2). Similarly, in the gender-disaggregated IV probit models presented in table 4, for both speciications 1 and 2 (adult children living respectively
near and with an elderly parent in self-assessed poor health), there is a statistically signiicant and positive association between the caregiver variable and the
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Variable
IV Probit
(2nd–stage Estimates: Probability of Migrating)
Speciication 1
Caregiver
Individual characteristics
Married
Education: senior high/tertiary
Employed in manufacturing
Employed in retail/inance
Employed in social services
Employed in other sectors
Employed in mining/construction
OLS
Speciication 1
Speciication 2
Male
Sample
(1)
Female
Sample
(2)
Male
Sample
(3)
Female
Sample
(4)
Male
Sample
(5)
Female
Sample
(6)
Male
Sample
(7)
Female
Sample
(8)
2.516***
(0.046)
–2.366***
(0.172)
3.470***
(0.153)
–3.340***
(0.320)
–0.004
(0.010)
0.004
(0.009)
0.008
(0.014)
–0.011
(0.013)
0.098*
(0.056)
–0.071
(0.081)
–0.018
(0.087)
0.116
(0.091)
0.051
(0.101)
–0.017
(0.084)
0.097
(0.101)
0.042
(0.059)
–0.224***
(0.070)
0.045
(0.083)
0.038
(0.107)
0.114
(0.090)
–0.049
(0.070)
0.123
(0.090)
0.063
(0.075)
–0.209
(0.254)
0.275***
(0.059)
–0.007
(0.086)
0.061
(0.093)
0.205*
(0.110)
0.099
(0.130)
0.027
(0.108)
0.114
(0.109)
0.092
(0.063)
–0.404***
(0.064)
0.036
(0.095)
0.080
(0.117)
0.125
(0.098)
0.013
(0.078)
0.102
(0.099)
0.124
(0.080)
–0.121
(0.291)
–0.008
(0.013)
–0.014
(0.020)
–0.001
(0.021)
0.030*
(0.016)
0.036**
(0.015)
0.051***
(0.014)
0.035
(0.021)
0.002
(0.014)
–0.023**
(0.010)
–0.005
(0.013)
0.024
(0.015)
0.008
(0.015)
–0.007
(0.012)
0.015
(0.014)
0.016
(0.011)
0.063
(0.040)
–0.007
(0.013)
–0.015
(0.020)
–0.001
(0.021)
0.030*
(0.016)
0.036**
(0.015)
0.051***
(0.014)
0.035
(0.021)
0.003
(0.014)
–0.024**
(0.010)
–0.005
(0.013)
0.024
(0.015)
0.008
(0.015)
–0.007
(0.012)
0.015
(0.014)
0.016
(0.011)
0.063
(0.040)
Anu Rammohan and Elisabetta Magnani
Education: elementary
Speciication 2
414
TABLE 4 Ins