1

The effect of Elderly Caregiving on Female Labour Supply in Indonesia

Elisabetta Magnani * and Anu RammohanMay 2006

**

In developing countries without universal social safety nets, coresidence between older

parents and adult children is a central feature of old-age security arrangements. However,

the impact of a deterioration in the health status on adult caregiving members’ labour

supply is less well-known. In this paper we use data from the 2000 Indonesian Family

Life Survey (IFLS 3), to examine the impact of caregiving for elderly household members

on the labour supply of coresident adults, particularly females. The empirical strategy in

this paper estimates an Instrumental probit model (IV probit) of labour force

participation, controlling for the possible endogeneity of elderly coresidence, using

instrumental variables. Estimation results show that for coresiding adult females,

caregiving for elderly household members significantly reduces the likelihood of

working. However, no such effects are observed for male members. This paper discusses

issue of results robustness.

Abstract

JEL classification: J12, O12

Keywords: elderly, caregiving, female labour

* School of Economics University of New South Wales. Tel: 61 2 9385 3370 E.mail: e.magnani@unsw.edu.au ** Discipline of Economics, University of Sydney, Sydney 2006. Tel: 61 2 9351 478. Email: a.rammohan@econ.usyd.edu.au

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1. Introduction

As fertility rates decline and life expectancy increases, the issue of funding the health and

retirement needs of a rapidly aging population gains added significance. However, in

developing countries, particularly in Asia, due to a lack of universal social safety nets, the

household continues to remain a key social and economic institution for its older

members. In particular, coresidence between older parents and at least one adult child is a

central feature of the familial support system of developing countries in Asia and

elsewhere (see Asis et al., 1995; Bongaarts and Zimmer, 2001; DaVanzo and Chan 1994;

Hermalin et al., 1996; Kim and Choe, 1992; Knodel et al, 1999; Natividad and Cruz,

1997). Besides financial support, many of the elderly will also need help in their daily

lives due to their frailty.

Furthermore, this family oriented support system for the elderly has been actively

encouraged by governments in many Asian countries (Chan, 1999; Knodel, 1999;

ESCAP, 1999), with few moves towards setting up universal social safety nets. However,

researchers such as Hugo (1996), have raised concerns on this traditional reliance on

family based support systems at a time of rapid demographic and economic changes in

the region. In particular, the household level link between population ageing, health status

among elderly, and its impact on the labour supply of coresident household members, in

an informal caregiving setting raises important issues. Specifically, the manner in which

the deterioration in the health status of elderly members stresses the household’s female

resources remains a less researched issue in the context of developing countries.

Previous research indicates that there is a gender dimension to informal caregiving, with

the burden of informal care typically falling on female household members. For example,

studies from the US show a strong negative effect on female labour supply from elderly

caregiving (see Moen et al., 1994; Ettner, 1995a, 1995b; Soldo and Wolf, 1994). Moen et

al (1994) report that in the US, over two-thirds of adult women aged 55-65 have been

caregivers to elderly household members at some point in their adult lives.

In this paper, we examine the impact of informal caregiving for the elderly on the labour

market behaviour of coresident adult household members using Indonesian data. These

issues are particularly important for a rapidly developing country such as Indonesia,

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which is experiencing rapid demographic changes with falling fertility rates, rising life

expectancy and a shrinking labour force. Despite the importance of research on

intergenerational economic relationships, particularly in the context of a rapid ageing of

the population in low and middle income countries, little is known about the nature of

such relationships in developing economies (Cameron and Cobb-Clarke, 2006).

Research on intrahousehold resource allocation from Indonesia has focused on the impact

of unemployment shocks on household’s health and education expenditure (Frankenberg

et al, 1999), consumption smoothing due to illness (Gertler et al, 2001; Gertler and

Gruber, 2001). Recent studies by Cameron and Cobb-Clarke (2006), Cai et al (2005),

study old-age security and the role of intergenerational transfers in the absence of a

public safety nets for Indonesia and China respectively. Moreover, studies from China

(Cai et al 2005, Giles and Mu, 2005), show that caregiving for elderly parents imposes

restrictions on the ability of adult children to migrate in search of better economic

opportunities. However, little is known about the issues surrounding the intra-household

resource allocation that ageing involves in Indonesia

Indonesia is a particularly interesting economy to study these issues for numerous

reasons. First, population censuses since the early 1970s show rising education levels,

delayed marriage and increasing workforce participation among Indonesian women,

particularly in Java and Bali. Second, family relationships in Indonesia, while still

modelled by a strong patriarchal principle, are changing rapidly, due to the speed of

economic growth as well as more subtle processes of cultural change (Hugo, 1996).

Third, rapid population ageing has become a major demographic preoccupation in

Indonesia in the twenty-first century. In Indonesia, over the last two decades, the average

number of children born per woman has declined from 4.1 in 1980 to 2.5 in 2000. At the

same time, life expectancy has increased from 56 to 67 years (UNDP, 2005). As a result

of these demographic shifts, the older age groups are growing both in absolute terms and

as a proportion of the population. For example, the Indonesian population aged 65 years

and older is expected to rise from 9.3 million in 2000 to 46.9 million in 2050,

representing about 20% of the Indonesian population (Edwards, 2003). Not surprisingly,

it is expected that the rapid ageing of the population will induce a drop in the overall

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number of labour force participants. This is also likely to increase the stress on the

household’s human resources, and as females are typically the informal caregivers to

elderly, population ageing at the household level is likely to have an adverse impact on

female labour force participation.

The analysis in this paper uses the rich Indonesian Family Life Survey 2000 to study the

impact of elderly caregiving on the labour supply of coresident household members. We

further examine if there are any gender differentials in labour supply due to caregiving

for elderly household members. The empirical strategy estimates an Instrumental

variables probit model where in stage one we estimate the probability of coresiding with

an elderly member, and conditional on this, in stage two we examine the effect of

caregiving (defined as coresiding with an elderly household member whose self-reported

health status is poor) on the labor supply of adult household members.

Our results show that caregiving for elderly household members, has a significantly

adverse impact on adult female household members in household with co-residing older

members when the care giving indicator variables is assumed to be endogenous. This

negative effect of caregiving on female labour supply also persists when we disaggregate

the sample by gender. In caregiving households, while the presence of young pre-school

age children reduces labour supply, an increase in the number of working male and

female household members facilitates labour supply. When the nature of endogeneity is

specified as derived from a selection process between co-residing households and non-co-

residing households, care giving to older family members fails to reach statistical

significance for labour supply decisions.

The rest of the paper is organized as follows. In the next section we present our modeling

strategy, which is followed by Section 3 where we describe the dataset used in the

analysis. The main results of the analysis are presented in Section 4, and the conclusions

are presented in section 5.

2. Modelling strategy

As in Wolf and Soldo (1994) we present a simple theoretical model in which a fixed total

time endowment T is allocated between alternative uses, namely “work”, care-giving CG

and a residual “leisure time”, L. Working hours are optimally chosen by an adult

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household member so to maximize a single period utility );,,,( HCOCGLcVV = , where

c is consumption, CG is care giving time spent by the household adult to care for his/her

elderly household member, 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 by the

elderly.

It is reasonable to assume that the price of CG is the forgone wage that would be earned

in the local labour market. There may also be monetary costs op associated with a unit of

market care CO. However, given the public good nature of this type of care, a market

price may not exist for all households. For this reason we prefer to proxy the cost of CO

by means of a set of household specific variables such as log of household assets,

whether the household experienced economic failure in the last five years, household

size, the presence of a maid in the household, number of working male and female

members, and the number of school and pre-school age children.

An important feature of care to the elderly is that it may be a joint decision by all children

of older members of the household. To get rid of the complexity involved in formalizing

an intra-household bargaining game, we assume that once the decision on elderly

member’s co-residence with an adult child has been made, this child will be in charge of

decision making regarding CO and CG. Given that we focus on the care decision taken

by households in a low income country such as Indonesia, we formalize the caring

decision in a slightly different way compared to Wolf and Soldo (1994). It is reasonable

to assume that CO and CG are not mutually exclusive, that is both can be positive at the

same time. In other words, the household that has elderly co-residing will provide CG>0

should the health situation of the elderly require care even when outside care is

purchased. Thus, the budget constraint a family adult faces is

)()( COpCGLTwc o−−−= .

The conceptual framework outlined above leads to a set of empirical questions, such as:

(i) how does care-giving to the elderly affect the working decisions of co-residing adult

members of the household? (ii) Does care-giving have gender differential effects on

labour supply?

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It should be noted here that while it could be argued that one response to a need for care-

giving to the elderly is to increase work hours and to allocate the additional wage income

to market-based care. However, in the Indonesian context, this may be difficult in remote

rural settings where there are no adequate facilities for old-age care. Furthermore, it is

likely that individuals are working in seasonal or informal labour markets in which there

are few prospects for changing work hours. While we do observe the presence of maids-

it is difficult to infer if they have been hired to specifically care for the elderly or if caring

for the elderly is an additional task.

In this setup it is clear that the decision to have the older member coreside with one of the

adult children has an opportunity cost that may be correlated with the decision to supply

labour services in the open market. In other words in estimating the impact of the

decision of providing care (CG=1) on adult member’s labour supply, we may need to

consider two important econometric issues: (i) the potential endogeneity of the decision

to provide care to an older member of the household; (ii) the potential selection bias

arising from the decision to coreside with older household members. It is these

econometric issues that we address in the following section.

3. Analytical strategy: the Econometric Specification

The objective of our analysis is to examine the impact of informal caregiving on labour

supply of adult household members, specifically females. It is however likely that the

selection of elderly into the household is not exogenous.

Unobservable factors potentially correlated with observations of parent health and the

labour supply decisions are a concern, and using predetermined household characteristics

alone will not solve these problems. Several sources of bias may be present. First, the

ability to observe labour market participation may reflect a potentially endogenous

decision for the household. For example, the presence of an elderly parent in the

household, may facilitate labour market participation if the elderly person provides some

form of informal child care. Alternatively, the adult child may be living in a parent’s

household because he/ she, may not have saved sufficient resources to set up a separate

household. In this case, the labour supply and co-residence decision may be

systematically related to financial constraints faced by the adult child, which may be

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related to their ability to participate in the labour market. Finally, an elder parent’s

residence in the household may reflect the outcome of a bargaining process among

siblings, with the household choosing to care for an ill parent making an implicit decision

to reduce participation in the labour market.

Here the number of siblings, who are either potential caregivers or potential recipients of

parental labor, will be systematically related to whether or not we observe an elderly

parent living in the household.

To address the potential endogeneity of the care-giving decision (CG=1) we model the

labour supply decision (E*=0, 1) of adult members of household with co-residing elderly

by running an Instrumental Variables probit (IV probit) model. The decision to supply

care to elderly members of the family is an observable indicator variable CG=1,0 that

assumes value one if the latent variable CG*=XB1+ε1

CG=1 if CG* >0 (1)

is positive and zero otherwise.

Formally:

CG=0 otherwise

where CG*=XB1+ε

E*=αCG+XB

1

2 + ε2

and the indicator variable CG is defined as follows:

(2)

1=CG if I([HH with elderly>50]=1) and H=poor (3a)

0=CG if I([HH with elderly>50]=1) and H=good (3b)

CG=missing if I([HH with elderly>50]=0 (3c)

By the argument discussed above, the error terms ε1 and ε2 in (1) and (2) may be

correlated, corr(ε1, ε2)=ρ a fact that would make the care giving decision endogenous.

Equation (1) formalizes the elderly member’s decision to care giving decision as a

function of a set X of household and individual-specific characteristics. The adult’s

labour supply decision is assumed to depend on CG, an indicator variable that assumes a

value of one if the elderly is co-residing with this adult and this older person needs care

due to health reasons. Note that we assume that H=poor is a random event, uncorrelated

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with household-specific fixed effects, conditional on the household decision to host an

older household member.

Our goal is to estimate the effect of providing care to the elderly on adult women’s labour

supply after correcting for the endogeneity and potential selection bias arising from the

formulation above.

The way the care giving variable indicator is specified in (3a-3c) above clearly illustrates

that the household for whom CG is non-missing may not be randomly drawn from a

sample of Indonesia household as the decision to having co-residing elderly may depend

on a complex set of family, financial and cultural reasons. For this reason, an interesting

comparison with the IV model described above is a Heckman selection model in which

the probability of observing a household with a co-residing person older than fifty is

explained by a set of variables that capture the complex decision of co-residence. Hence,

in an Heckman selection model the selection (first stage) regression estimates the

probability of a household having an older member co-residing, that is I([HH with

elderly>50]=1), as follows:

1iitititit )V ,F ,C ,H(1)50]elderly with [HH(Pr ε+==> fIob (5)

where Hit is a set of variables that capture the demographic composition of household i in

time t, t=2000; Cit is a set of characteristics specific to the head of household i, such as

his/her education, age, whether female, whether he/she is living with a spouse; Fit

includes, among others, a dummy variable for whether the household has a health card, a

dummy for whether the house is self-owned, the value of assets (in log form); a set Vit

includes a dummy variable for whether the village is in a rural area, whether it has

electricity connection, whether there is public transport available. In this econometric

specification, the first stage regression estimates the probability of a household having a

coresiding member who is 50 or plus of age. The second stage regression specifies a

labour supply decision as a function of the household care-giving indicator variable

(CG=0,1), after adjusting for the selection among household induced by the decision to

co-reside with an older member of the family [I(HH with elderly>50)=1]. Specifically,

the second stage regression estimates a Probit model for the probability that the adult’s

working hours (in log transformation) are positive. A plot of the log hours worked series

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shows that the distribution across a sample of adults is skewed towards zero. We then

redefine the dependent variable by means of an indicator variable that takes value one if

the individual works at least 10 (normal) hours per week. Similarly, the augmented

specification for working hours is

1iititc )m ,(]Hhours ngProb[Worki ε+=> Xf , cH =10 (6)

Where Xit comprehends a number of variables that capture the financial and demographic

structure of the household, a set of individual specific variables as well as a set of

variables that capture the conditions of the local labour markets that an individual faces,

which may affect their decision to work. The focus of our analysis is whether the

existence of caring commitment with respect to some older members of the family

significantly impact upon labour supply decisions of the co-residing female adults. Thus

the set Xit

4. Data

includes the indicator variable (CG=0, 1). To facilitate comparisons with

males, we estimate the regressions for the combined sample, and then separately for male

and female sample samples.

The data for this study comes from the third wave of the Indonesian Family Life Survey

(IFLS3), which was conducted in 2000. The IFLS is a random sample survey, covering

thirteen provinces where approximately 83 percent of the population resides. The

survey collects data on individual respondents, their households, communities, and the

health and education facilities they use.

The IFLS dataset is rich and unique as it contains detailed information on household’s

demographic, labour market and economic characteristics expenditures on consumption

and health, access and utilization of health care facilities and availability of any social

safety nets. The IFLS dataset also has the advantage of providing a much richer picture of

households, and health status than is typically available in household surveys. One

important drawback of the IFLS is that it does not contain specific and detailed

information about the use of time of household members. As a result, we cannot directly

estimate the intensity of care provided to the household’s elderly, but simply whether

care is provided. To do so we combine information of coresidence with information on

health as supplied by household’s members. For example, it has information on a range

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of health related issues such as the self-reported health status and the level of disability

experienced by household members as measured by ADLs (Activities of Daily Living).

The household is our unit of analysis. We define as elderly those persons who were over

50 years of age in 2000. Our choice of 50 years as a lower bound for our definition of

elderly is motivated by the fact that in the IFLS dataset, this is the age from which data on

health is available for older individuals. Furthermore, given the current life-expectancy in

Indonesia of 67 years, an individual at age 50 can reasonably be regarded as elderly.

Our main variables of interest are to identify households that are care-giving households,

identify some health measures for elderly household members and to get information on

the labour market status of adult household members. Hence, we have two categories of

dependent variables- (1) a measure for caregiving household and (2) a measure for labour

supply.

It is important to note that there is no question in the IFLS-3 that directly distinguishes

between care-giving and non-care-giving households. Hence, following Wolf and Soldo

(1994), we define a care-giving household by means of an indicator variable, namely

Care-giving HH, which takes the value one if two conditions are simultaneously satisfied:

(i) there is at least one household member over the age of 50 [HH with elderly>50], and

(ii) the elderly household member’s self-reported health status is described as being poor.

This variable is defined as follows. Respondents aged 50 and above were asked, ‘how do

you expect your health to be next year?’ Those who responded somewhat worse and

much worse than this year were recorded as 1, not being satisfied with their health.

In dealing with endogeneity, we note that both of our dependent variables are binary

variables. Hence, we estimate a probit model with endogenous binary regressors and use

the ordinary least square results for comparison. To deal with the potential endogeneity of

the coresidence between elderly and adult children, we follow Wolf and Soldo (1994) and

introduce several instruments such as parental education, employment and non-coresiding

sibling’s characteristics. These variables affect coresidence decisions between elderly

parents and adult children, but do not directly influence their labour force participation.

Table 1 reports the summary statistics for a select group of variables used in the IV probit

estimations for the full-sample, and disaggregated by gender and caregiving status. Our

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analysis is based on data for 17549 individuals who are in the working age group of 15-

50 years of age and for whom there is complete information available on a range of

individual, demographic, household and economic characteristics. Approximately 34

percent of respondents live in households with elderly members who felt unsatisfied

about their health conditions (caregiving households by our definition). When we

disaggregate the sample by the caregiving status of the individual, it is interesting to note

that in general the characteristics of the sample are similar. However, we observe that a

greater proportion of caregiving individuals live in IDT1

From Table 1 it is interesting to observe that although a greater proportion of caregiving

individuals (21 percent) live in IDT villages compared with 19 percent among non-

caregivers, caregiving households in general have slightly better access to village level

infrastructure relative to non caregiving households. For example, caregiving households

have approximately 1 percent greater access to electricity relative to non-caregiving

households. This advantage in also observed with regard to access to piped water, public

transport and asphalt roads.

villages, classified as backward

villages using the Indonesian government’s definition.

In terms of household characteristics, according to Table 1 caregiving households have a

slightly lower proportion of pre-school age children, less likely to have experienced

economic failure and have a maid. Not surprisingly, a higher proportion of caregiving

households have elderly parents coresiding with them relative to non-caregiving

households. We use limitations to the ability to perform basic activities of daily living

(ADLs) as an explanatory variable to denote the health status of the caregiving

respondent, which can impact on their caregiving status. The ADL is a count of the

number of ADL activities that the respondent is limited to due to illness. Ten ADLs are

included in the analysis and each of these carry an equal weight. They include the

following daily activities: (i) to carry a heavy load for 20 meters; (ii) to walk for 5

kilometers; (iii) to walk for 1 kilometer; (iv) to bow, squat, kneel; (v) to sweep the house

floor yard; (vi) to draw a pail of water from a well; (vii) to stand up from sitting on the

floor without help; (viii) to stand up from sitting position in a chair without help; (ix) to 1 IDT (Inpres Desa Tertinggal) is a governmental program that provided poor villages with grants from 1994-95 through 1996-97.

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go to the bathroom without help; (x) to dress without help. For each activity an indicator

variable takes on a value of 1 if the individual can perform the activity without difficulty

and zero if the individual cannot perform it. ADL is the sum of all the positive indicator

variables.

Indonesia’s inner islands (Sumatra, Java, Bali) are more populated and relatively more

developed relative to its outer islands (Sulawesi, NTB, etc). Hence, relatively more

development and poverty alleviation programs are directed to these islands due to their

denser population and higher poverty levels. Accordingly, regional dummy variables

are created with Java or Bali as the base. Around 16 percent of the population lives in

outer islands (other than Java, Bali and Sumatra) and 20 percent lives in Sumatra.

We control for rural/urban residence and also take into account community/village level

development by including indicator variables for whether or not the village is a backward

village (IDT)2

5. Results

, whether residents in the village have access to electricity, asphalt road and

public transport system and a dummy variable to indicate whether the household has

access to piped water. Education is found to be a key factor in influencing labour force

participation and in the likelihood of becoming caregivers. In our dataset, educational

attainment is measured in terms of highest level of education attained.

As discussed above, the principal aim of this paper is to examine the effect of caregiving

on labour supply of female coresident household members. Hence, we present results for

the full sample and then report regression results disaggregated by gender to examine if

there are gender differential effects of caregiving on labour supply. The main results of

the analysis are presented in Tables 2-5 of the Appendix. The sample is restricted to

individuals that are coresiding with elderly members. The IV-probit results for the full

sample and the disaggregated samples are presented in Tables 2 and 3 respectively. In

tables 4 and 5 we present results on the effect of caregiving on hours of work for these

three samples (IV-2SLS).

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In table 2, the left hand side panel presents the first stage regressions results for the

indicator variable I(Care-giving HH=0, 1). The right hand side variable presents the

second stage results for labour force participation. Table 3 compares the labour force

participation decisions for males and females estimated by means of instrumental

variables. The scope of Table 3 is to examine if there are any differential effects on males

and females from belonging to care-giving households. In both tables 2 and 3 the

explanatory variables are organized in four groups, namely Individual Specific Variables,

Household Composition Variables, Household Financial Variables, Extended Family

Network, Additional Instruments (for first stage regressions only).

To show the reliability of our results, we first discuss the impact of these sets of variables

on the participation decision (second stage regressions) and then we proceed by

commenting on the impact of I(Care-giving HH=0, 1) on labour force participation.

Hence, we first discuss the IV probit results in detail and then compare the IV probit

model to the IV OLS or IV2SLS. The first point to note is the statistically significant

effect of the Inverse Mills Ratio for both the full sample and the female only sample. This

indicates that the second stage results for both sets of models are consistent.

Consistent with our expectations, table 2 shows that care-giving activities has a

significant and negative effect on the labour force participation of adults in the full

sample. Table 3 illustrates however, that this full sample result hides important

differences in the way caring for the elderly members of the family impacts upon males

and females’ participation decisions separately (see columns [1] and [2]). In particular,

while the probability of participation increases in a sample of male adults (the level of

significance is just above ten percent), female participation is significantly reduced by

caregiving activities.

Age is consistently important in determining the labour force participation decision in the

full sample as well as in samples of males and females only. Our results indicate that

relative to younger age cohorts (15-25 year olds), older individuals are significantly more

likely to be in the labour force. While the health status of the adult respondents in the full

sample is positively signed and statistically significant for the decision to participate,

there are remarkable differences in the way health impacts upon male and female

14

participation when we examine the two samples separately. Table 3 illustrates that the

range of activities of daily living the person can perform (ADL) positively impacts on

male participation, but it does not affect female participation.

While education has a significant effect on female labour force participation (but no

effect on male participation decisions), the sign of the coefficient is contrary to what we

expect. In the female sample (table 3), the significant and negative effect on labour

market participation of being a primary and junior high graduate (relative to a female

with no schooling) may seem surprising at first. However, this result is consistent with

the findings of Widarti (1998), who has identified a J-shaped curve, where less educated

women in Jakarta, have labour force participation rates that are close to those found

among well-educated women, whereas moderately educated women have much lower

labour force participation rates.

The household composition is also an important factor in determining labour force

participation by adults. Tables 2 and 3 show that in caregiving households, the presence

of young children (both school age and pre-school age), significantly reduces female

labour force participation. Although these variables are consistently negatively signed in

the full sample and in a sample of males only, table 3 shows that the presence of children

is much less important in determining male labour force participation (the size of these

coefficient drops while their standard errors increase).

In the group of variables that capture the household financial situation it is worth

mentioning that the experience of economic failure negatively affects male participation

but it is not statistically significant in a sample of female adults. The number of employed

males positively impacts on male participation, but it does not affect female participation,

which however positively responds to the number of employed females in the household.

The importance of the family network for male and female labour force participation

greatly outweighs the impact of labour market conditions. Tables 2 and 3 show that being

committed to financially help other non-resident siblings, increases both male and female

participation. However, being a recipient of financial help from other non-coresiding

members reduces male participation, but does not affect female participation.

15

In terms of regional factors, both in the full sample and in the female sample, relative to

women from Java and Bali, Sumatran women are significantly more likely to be observed

working.

We may wonder to explore whether the negative impact of care giving on labour supply

as described in these tables still exists once we take into account the selection bias that

the decision to coreside with elderly members of the extended family may introduce.

Table 6 summarizes the main results found when estimating a Heckman selection model

as described in the previous sections. Although the selection models fails to deliver Mills

Ratios that are statistically significant, the relevant result is that the health status of older

members of the household now is consistently non-statistically significant for adults’

labour force participation in all samples in all specifications. As the Heckman model is

highly sensitive to specification of the model further investigation will need to be done on

this issue. However, the IV and Heckman results combined shed important light on the

way the health status of older family members may impact upon adults’ labour supply

decision. These tables suggest that the co-residence decision may select household that

have a lower opportunity costs in terms of lost labour market opportunities so that the

effect of care giving has a negligible effect on adult labour supply in the care-giving

households.

6 Conclusions

In developing countries in the absence of universal social safety nets, co-residence

between elderly and adult members is a central feature of old-age security. However, the

manner in which deterioration in the health of elderly members imposes restrictions on

the labour force participation of co-residing adults has been less explored in the literature.

However, elderly care giving is likely to have a greater adverse effect on the household’s

female members. To explore this issue, in this paper we examine the effect of care giving

for elderly sick household members on the labour supply of co-resident adult household

members using Indonesian data. Our estimation results show that when endogeneity

issues are taken into account, care giving for elderly household members adversely

affects female labour force participation. Furthermore, female adults co-residing with

elderly household members are much less likely to be in the labour force. Our results

16

further indicate that relative to single women married women are more likely to be

adversely affected by care giving for elderly members. Finally, the importance of the

family networks for male and female labour force participation greatly outweighs the

impact of labour market conditions.

These results also point to the importance of understanding how the decision of co-

residence with older members of the household occurs as the above results do not appear

to be robust to a consideration of the impact of selection. From a policy perspective,

addressing these issues will be of paramount importance to address the question of how

to fund the retirement and health of a rapidly ageing population while boosting adults’

labour supply.

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18

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19

Appendix

Table 1: Selected summary statistics for full-sample and by caregiving status

Full sample

N=17548

Caregiver CG=1

N= 3919

Non-caregiving CG=0 or missing

N= 15500 Proportions Proportions Proportions Caregiving 0.339 Restricted hours of work 0.633 0.656 Individual characteristics Female 0.55 0.543 0. Respondent’s age 26-35 0.273 0.294 0.262 age 36-45 0.177 0.171 0.179 age 46+ 0.129 0.110 0.139 Religion- Islam 0.881 0.885 0.880 Marital status- married 0.532 0.496 0.551 separated/divorced/widowed 0.052 0.056 0.049 Education- primary school 0.369 0.346 0.38 Education- junior high 0.182 0.185 0.180 Education- senior high/ college 0.385 0.414 0.371 Help non-coresident siblings 0.419 0.420 0.417 Receive help from non-coresident sibling 0.469 0.496 0.455 Household characteristics Owns house 0.92 0.919 0.934 Household experienced economic failure in the last five years 0.058 0.052 0.060 Household has maid 0.030 0.025 0.032 Mother coresides 0.743 0.758 0.736 Father coreside 0.784 0.785 0.783 Household has health card 0.201 0.213 0.195 Community characteristics Rural residence 0.468 0.470 0.467 Sumatra 0.196 0.238 0.175 Outer island 0.198 0.210 0.191 IDT village 0.213 0.206 0.0.17 asphalt road 0.758 0.791 0.741 Electricity 0.912 0.920 0.908 piped water 0.546 0.548 0.545 public transport 0.713 0.733 0.703 Mean (SD) Mean (SD) Mean (SD) Household head’s age 55.50(11.161) 57.095(11.383) 54.690 (10.959) No. of female household members working 0.821 (0.812) 1.099 (0.919) 0.784 (0.989) No. of male household members working 1.251 (0.878) 1.501 (0.966) 1.600 (0.860) Log asset 16.780 (1.499) 16.793 (1.495) 16.774 (1.502) Household size 7.453 (2.750) 7.479 (2.653) 7.440 (2.798) No. of pre-school age children 0.473(0.704) 0.464(0.688) 0.478(0.711)

20

Table 2: Effect of caregiving on probability of working, assuming endogeneity: Full sample Full Sample 1st 2 stage estimates nd

stage estimates

[1] [2] Coefficient (Std. Error) Coefficient (Std. Error) Caregiving^ -0.916*** (0.240) Individual Characteristics Female -0.018 (0.013) -0.920*** (0.065) Respondent’s Age 26-35 0.053*** (0.017) 0.657*** (0.056) Respondent’s Age 36-45 0.029 (0.022) 0.834*** (0.076) Respondent’s Age 46+ -0.012 (0.026) 0.753*** (0.086) Activities of Daily Living (ADL) -0.011 (0.007) 0.107*** (0.023) Marital status- married -0.024 (0.019) 0.253*** (0.060) Marital status- separated/divorced/widowed -0.001 (0.031) 0.427*** (0.094) Education- primary school 0.021 (0.027) -0.041 (0.083) Education- junior high 0.038 (0.030) -0.113 (0.093) Education- senior high/ college 0.044 (0.030) -0.157* (0.093) Household Characteristics Household size 0.000 (0.003) -0.097*** (0.012) No. of school going children -0.009 (0.013) -0.199*** (0.039) No. of female children below 14 years of age 0.016 (0.014) 0.258*** (0.043) No. of male children below 14 years of age 0.006 (0.014) 0.275*** (0.044) No. of pre-school children -0.003 (0.015) -0.257*** (0.045) Maid -0.045 (0.037) 0.224* (0.116) Household’s Financial Situation Whether household experienced economic failure in the last 5 years -0.013 (0.026) -0.133* (0.078) Log Assets 0.006 (0.005) -0.020 (0.014) No. of female employed household members 0.021** (0.007) 0.621*** (0.046) No. of male employed household members -0.026*** (0.007) 0.225*** (0.029) Family and Social Networks Help non-coresident sibling 0.009 (0.013) 0.298*** (0.041) Receive help from non-coresident sibling 0.036*** (0.013) -0.083** (0.041) Community Characteristics Rural 0.008 (0.016) 0.031 (0.041) Sumatra 0.116*** (0.017) 0.253*** (0.052) Outer island 0.068*** (0.017) 0.132** (0.051) Instrumental Variables Household head’s age 0.004*** (0.001) Household has health card 0.031** (0.015) Household lives in own house -0.050** (0.025) No. of living non-coresident siblings 0.005 (0.003) Father’s highest education- primary -0.041** (0.017) Father’s highest education- higher than primary -0.025 (0.023) Contd. on the next page

21

Father work status- retiree 0.038** (0.018) Father work status- unemployed 0.003 (0.030) Father work status- sick 0.035 (0.029) Father work status- other -0.015 (0.033) Mother highest education- primary 0.018 (0.019) Mother highest education- higher than primary 0.110*** (0.042) Mother work status- housekeeping -0.071*** (0.015) Mother work status- retiree -0.052** (0.026) Mother work status- unemployed -0.045 (0.032) Mother work status- sick -0.095** (0.042) Father coresides 0.030 (0.018) Mother coresides 0.028 (0.019) IDT village -0.007 (0.015) Asphalt road 0.079*** (0.019) Electricity 0.003 (0.029) Piped water -0.031 (0.016) Public transport 0.013 (0.016) Rho 0.429*** (0.113) Wald statistics 11.020 (0.000) (p-value) log L -6,743.865 N 6,049

Note: ***, ** and * refer to 1%, 5% and 10% levels of significance. Standard errors are in parentheses. The dependent variable in [1] is an indicator variable HH with elderly>50=1 if the respondent coresides with an elderly member, HH with elderly>50 =0 otherwise. The dependent variable in [2] is an indicator variable I=1 if the respondent is working for at least 10 (normal) hours per week, I=0 otherwise. The sample is restricted to household with co-resident elderly members.

22

Table 3: Effect of caregiving on probability of working, assuming endogeneity (IV probit model): Male and female samples 2nd stage estimates (Std. Error) 1st stage estimates (Std. Error) Female sample Male sample Female sample Male Sample [1] [2] [3] [4] Caregiving^ -1.152*** (0.281) 0.740 (0.470) Individual Characteristics Respondent’s Age 26-35 0.592*** (0.077) 0.661*** (0.121) 0.068*** (0.024) 0.033 (0.025) Respondent’s Age 36-45 0.788*** (0.110) 0.632*** (0.172) 0.005 (0.029) -0.070* (0.036) Respondent’s Age 46+ 0.798*** (0.121) 0.612** (0.194) -0.017 (0.032) -0.008 (0.044) Activities of Daily Living (ADL) 0.066** (0.029) 0.144*** (0.046) -0.019** (0.009) 0.003 (0.013) Marital status- married 0.035 (0.075) 1.165*** (0.130) -0.035 (0.026) -0.027 (0.030) Marital status- separated/divorced/widowed 0.260** (0.114) 0.198 (0.215) 0.001 (0.037) -0.023 (0.061) Education- primary school -0.182** (0.092) 0.092 (0.256) 0.002 (0.031) 0.093* (0.056) Education- junior high -0.291*** (0.110) -0.104 (0.261) 0.006 (0.037) 0.112* (0.058) Education- senior high/ college -0.148 (0.106) -0.334 (0.256) 0.007 (0.036) 0.119** (0.058) Household Characteristics Household size -0.092*** (0.017) -0.127*** (0.022) 0.003 (0.005) -0.003 (0.005) No. of school age children -0.215*** (0.058) -0.177** (0.077) 0.015 (0.018) -0.037** (0.019) No. of female children below 14 years of age 0.293*** (0.064) 0.194** (0.080) -0.006 (0.019) 0.040** (0.020) No. of male children below 14 years of age 0.297*** (0.066) 0.266*** (0.081) -0.017 (0.019) 0.027 (0.021) No. of pre-school children -0.360*** (0.071) -0.088 (0.082) 0.016 (0.020) -0.032 (0.021) Maid 0.154 (0.142) 0.385* (0.228) -0.037 (0.047) -0.066 (0.063) Household Financial Situation Household experienced economic failure in the last 5 years -0.037 (0.108) -0.368*** (0.134) -0.015 (0.037) -0.017 (0.037) Log Asset -0.019 (0.018) -0.037 (0.025) 0.004 (0.007) 0.013* (0.007) No. of female employed household members 0.800*** (0.088) 0.149*** (0.042) 0.020** (0.009) -0.021* (0.012) No. of male employed household members -0.009 (0.030) 0.747*** (0.066) -0.026*** (0.004) -0.015 (0.011) Family and Social Networks

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Help non-coresident sibling 0.299*** (0.053) 0.386*** (0.074) 0.030* (0.017) -0.016 (0.019) Receive help from non-coresident sibling -0.024 (0.051) -0.264*** (0.068) 0.022 (0.017) 0.057*** (0.019) Community Characteristics Rural 0.029 (0.053) -0.003 (0.075) 0.025 (0.022) 0.000 (0.024) Sumatra 0.197*** (0.068) 0.181 (0.115) 0.110*** (0.023) 0.128*** (0.025) Outer island 0.024 (0.067) 0.150 (0.105) 0.064*** (0.023) 0.070*** (0.026) Instrumental Variables Household head’s age 0.003*** (0.001) 0.006*** (0.001) Household has health card 0.031 (0.020) 0.048** (0.022) Household lives in own house -0.029 (0.032) -0.092** (0.038) No. of living non-coresident siblings 0.003 (0.004) 0.011 (0.006) Father’s highest education- primary -0.039* (0.022) -0.016 (0.030) Father’s highest education- higher than primary -0.003 (0.004) -0.028 (0.039) Contd. on the next page

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Father work status- retiree 0.045** (0.023) 0.058* (0.030) Father work status- unemployed -0.006 (0.037) 0.045 (0.050) Father work status- sick 0.024 (0.034) 0.112** (0.053) Father work status- other 0.019 (0.043) -0.046 (0.052) Mother highest education- primary 0.015 (0.024) 0.031 (0.032) Mother highest education- higher than primary 0.183*** (0.058) 0.048 (0.060) Mother work status- housekeeping -0.075*** (0.019) -0.054** (0.022) Mother work status- retiree -0.061** (0.031) -0.003 (0.045) Mother work status- unemployed -0.052 (0.039) -0.028 (0.056) Mother work status- sick -0.056 (0.050) -0.172** (0.077) Father coreside -0.035 (0.023) -0.021 (0.030) Mother coreside 0.029 (0.023) 0.026 (0.032) IDT village -0.009 (0.019) 0.018 (0.024) Asphalt road 0.077*** (0.024) 0.095*** (0.029) Electricity -0.002 (0.037) 0.028 (0.047) Piped water -0.023 (0.021) -0.033 (0.023) Public transport 0.009 (0.020) 0.002 (0.025) Rho 0.540*** (0.131) -0.347 (0.223) Wald statistics (p-value) 10.72 (0.001) 2.04 (0.153) log L -3718.911 -2627.976 N 1,841 2,078 3340 2709 Note: ***, ** and * refer to 1%, 5% and 10% levels of significance. Standard errors are in parentheses. Columns [1] and [2] refer to second stage estimates of female and male sample respectively and[3] and [4] refer to first stage estimates for female and male samples respectively. Second-stage dependent variable: I=1 if the respondent is working for at least 10 (normal) hours per week, I=0 otherwise. First-stage dependent variable: The dependent variable in [1] is an indicator variable HH with elderly>50=1 if the respondent coresides with an elderly member, HH with elderly>50 =0 otherwise. The sample is restricted to households with co-resident elderly members.

25

26

Table 4: Effect of caregiving on working hours, assuming endogeneity (IV 2SLS model): Full sample Full Sample 1st 2 stage estimates nd

stage estimates

[1] [2] Coefficient (Std. Error) Coefficient (Std. Error) Caregiving^ 0.169 (0.112) Individual Characteristics Female -0.007 (0.018) -0.089*** (0.019) Respondent’s Age 26-35 0.058*** (0.022) 0.018 (0.024) Respondent’s Age 36-45 0.038 (0.028) -0.004 (0.029) Respondent’s Age 46+ 0.014 (0.031) -0.059* (0.032) Activities of Daily Living (ADL) -0.000 (0.001) 0.012 (0.011) Marital status- married -0.030 (0.024) 0.073*** (0.024) Marital status- separated/divorced/widowed -0.014 (0.037) 0.046 (0.040) Education- primary school 0.030 (0.031) 0.012 (0.034) Education- junior high 0.059 (0.036) 0.001 (0.039) Education- senior high/ college 0.052 (0.036) -0.138*** (0.038) Household Characteristics Household size 0.003 (0.004) 0.010** (0.005) No. of female children below 14 years of age -0.000 (0.017) -0.024 (0.019) No. of male children below 14 years of age -0.011 (0.017) -0.033* (0.019) No. of pre-school children 0.011 (0.016) 0.020 (0.020) Maid -0.088* (0.046) 0.114** (0.050) Household Financial Situation Household experienced economic failure in the last 5 years 0.001 (0.033) 0.036 (0.036) Log Asset 0.009 (0.006) 0.015** (0.006) No. of female employed household members -0.027*** (0.010) 0.006 (0.010) No. of male employed household members -0.028*** (0.009) -0.010 (0.010) Family and Social Networks Help non-coresident sibling 0.011 (0.016) 0.050*** (0.017) Receive help from non-coresident sibling 0.039** (0.016) -0.059*** (0.018) Community Characteristics Rural 0.004 (0.020) -0.147*** (0.018) Sumatra 0.134*** (0.211) -0.055** (0.026) Outer island 0.066*** (0.021) -0.030 (0.024) Instrumental Variables Household head’s age 0.005*** (0.001) Household has health card 0.046** (0.019) Household lives in own house -0.063** (0.032) No. of living non-coresident siblings 0.004 (0.004) Father’s highest education- primary -0.040* (0.022) Father’s highest education- higher than primary -0.035 (0.031) Father work status- retiree 0.034 (0.023) Contd. on the next page

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Father work status- unemployed 0.010 (0.037) Father work status- sick 0.050 (0.036) Father work status- other -0.013 (0.046) Mother highest education- primary 0.053** (0.024) Mother highest education- higher than primary 0.116** (0.055) Mother work status- housekeeping -0.039** (0.019) Mother work status- retiree -0.024 (0.032) Mother work status- unemployed -0.015 (0.040) Mother work status- sick -0.109** (0.052) Father coreside -0.034 (0.023) Mother coreside 0.026 (0.024) IDT village 0.020 (0.020) Asphalt road 0.074*** (0.023) Electricity -0.010 (0.038) Piped water -0.029 (0.020) Public transport 0.015 (0.020) N 3,919

Note: ***, ** and * refer to 1%, 5% and 10% levels of significance. Standard errors are in parentheses. The dependent variable in [1] is an indicator variable HH with elderly>50=1 if the respondent coresides with an elderly member, HH with elderly>50 =0 otherwise. The dependent variable in [2] is the log transformation of hours of work, with hours of work ≥10 a week The sample is restricted to household with co-resident elderly members.

28

29

Table 5: Effect of caregiving on restricted normal working hours, assuming endogeneity (IV-2SLS model): Male and female samples

2nd 1 stage estimates (Std. Error) st stage estimates (Std. Error) Female sample Male sample Female sample Male sample [1] [2] [3] [4] Caregiving^ 0.214 (0.184) 0.179 (0.111) Individual Characteristics Respondent’s Age 26-35 -0.014 (0.043) 0.038 (0.029) 0.088*** (0.034) 0.044 (0.028) Respondent’s Age 36-45 -0.012 (0.045) -0.027 (0.039) 0.038 (0.040) 0.074* (0.039) Respondent’s Age 46+ -0.039 (0.049) -0.078* (0.045) 0.051 (0.044) 0.003 (0.047) Activities of Daily Living (ADL) -0.039 (0.042) 0.157*** (0.030) -0.036 (0.037) -0.023 (0.032) Marital status- married -0.018 (0.055) 0.077 (0.069) -0.030 (0.047) 0.005 (0.068) Marital status- separated/divorced/widowed 0.018 (0.015) -0.005 (0.017) -0.013 (0.012) 0.019 (0.016) Education- primary school -0.001 (0.044) -0.000 (0.059) 0.002 (0.038) 0.119** (0.059) Education- junior high 0.006 (0.056) -0.024 (0.063) 0.043 (0.049) 0.131** (0.062) Education- senior high/ college -0.142*** (0.053) -0.155** (0.063) 0.002 (0.047) 0.148** (0.062) Household Characteristics Household size 0.003 (0.007) 0.009** (0.006) 0.003 (0.006) 0.003 (0.006) No. of female household members below 14 years of age -0.018 (0.030) -0.016 (0.024) 0.015 (0.025) -0.014 (0.024) No. of male household members below 14 years of age -0.046 (0.030) -0.004 (0.024) -0.018 (0.024) -0.009 (0.024) No. of pre-school children 0.028 (0.032) 0.001 (0.025) 0.027 (0.025) 0.001 (0.024) Maid 0.167** (0.070) 0.012 (0.077) -0.081 (0.059) -0.115 (0.076) Household Financial Situation Household experienced economic failure in the last 5 years 0.053 (0.058) 0.017 (0.045) -0.014 (0.050) 0.011 (0.045) Log Asset 0.032*** (0.010) -0.002 (0.008) 0.010 (0.009) 0.001 (0.009) No. of female employed household members -0.014 (0.017) 0.008 (0.013) -0.027* (0.015) -0.029** (0.013) No. of male employed household members -0.001 (0.017) 0.007 (0.014) -0.041*** (0.014) -0.016 (0.014)

30

Family and Social Networks Help non-coresident sibling 0.002 (0.029) 0.090*** (0.023) 0.063*** (0.023) -0.035 (0.022) Receive help from non-coresident sibling -0.035*** (0.027) -0.081*** (0.023) 0.016 (0.023) 0.064*** (0.022) Community Characteristics Rural -0.127*** (0.029) -0.165*** (0.023) 0.001 (0.030) 0.009 (0.027) Sumatra -0.094** (0.041) -0.018 (0.032) 0.129*** (0.032) 0.144*** (0.029) Outer island -0.066* (0.036) -0.001 (0.030) 0.042 (0.032) 0.092* (0.030) Instrumental Variables Household head’s age 0.003*** (0.001) 0.006*** (0.001) Household has health card 0.044 (0.029) 0.056** (0.027) Household lives in own house -0.029 (0.045) -0.094** (0.045) No. of living non-coresident siblings -0.006 (0.006) 0.014** (0.006) Father’s highest education- primary -0.072** (0.031) -0.017 (0.031) Father’s highest education- higher than primary -0.017 (0.047) -0.054 (0.043) Father work status- retiree 0.022 (0.032) 0.036 (0.033) Father work status- unemployed -0.021 (0.052) 0.097* (0.055) Father work status- sick 0.013 (0.047) -0.036 (0.063) Father work status- other 0.030 (0.070) -0.011 (0.051) Mother highest education- primary 0.061 (0.035) 0.039 (0.035) Mother highest education- higher than primary 0.200** (0.092) 0.074 (0.070) Mother work status- housekeeping -0.043 (0.029) -0.032 (0.052) Mother work status- retiree -0.025 (0.042) -0.035 (0.026) Mother work status- unemployed -0.022 (0.055) -0.008 (0.048) Mother work status- sick -0.034 (0.069) -0.215*** (0.082) Father coreside -0.064 (0.033) -0.020 (0.033) Mother coreside 0.023 (0.034) 0.033 (0.034) IDT village 0.013 (0.029) 0.024 (0.027) Asphalt road 0.053 (0.035) 0.092*** (0.032) Electricity -0.010 (0.054) -0.017 (0.054) Piped water 0.002 (0.030) -0.048* (0.026)

31

Public transport 0.024 (0.030) 0.007 (0.028) N 1,841 2,078

Note: ***, ** and * refer to 1%, 5% and 10% levels of significance. Standard errors are in parentheses. Columns [1] and [2] refer to second stage estimates of female and male sample respectively and[3] and [4] refer to first stage estimates for female and male samples respectively. Second-stage dependent variable: log transformation of hours of work. First-stage dependent variable: The dependent variable in [1] is an indicator variable HH with elderly>50=1 if the respondent coresides with an elderly member, HH with elderly>50 =0 otherwise. The sample is restricted to household with co-resident elderly members.

32

Table 6: Effect of caregiving on restricted working hours, assuming endogeneity: Male and female samples, Heckman selection model. Selected second stage explanatory variables. Labour Force Participation decision Full sample Male sample Female sample Caregiving 0.005 (0.043) 0.090(0.0818) 0.008 (0.059) Female -1.022(0.048)*** Activities of Daily Living (ADL) 0.143(0.023)*** 0.137(0.051)*** 0.133(0.293)*** Log Asset -0.031(0.016)** -0.038(0.029) -0.039(0.021)* No. of female employed household members 0.827(0.027)*** 0.179(0.050)*** 1.197(0.040)*** No. of male employed household members 0.357(0.024)*** 1.134(0.056)*** 0.021(0.034) Household size -0.127(0.012)*** -0.172(0.022)*** -0.133(0.016)*** No. of female household members below 14 years of age 0.322(0.046)*** 0.332(0.083)*** 0.394(0.066)*** No. of male household members below 14 years of age 0.339(0.046)*** 0.400(0.085)*** 0.406(0.065)*** No. of pre-school children -0.324(0.049)*** -0.285(0.091)*** -0.458(0.070)*** Help non-coresident siblings 0.348(0.043)*** 0.437(0.082)*** 0.354(0.059)*** Receive help from non-coresident siblings -0.111(0.042)*** -0.225(0.081)*** -0.025(0.058) Rural 0.097(0.046)** 0.108(0.091) 0.081(0.062) Modified Mills Ratio -0.009 0.050 -0.027 N 17549 8270 9279 Uncensored N 6049 2709 3340

Labour Force Participation decision

(restricted to working hours>10) Full sample Males Females Caregiving -0.014 (0.041) 0.024 (0.072) 0.015 (0.055) Female -1.001(0.046)*** Activities of Daily Living (ADL) 0.130(0.022)*** 0.152***(0.045) 0.105(0.028)*** Log Asset -0.25(0.015)* -.0359 (0.026) -0.024(0.020) No. of female employed household members 0.697(0.025)*** 0.161*** (0.044) 0.962(0.034)*** No. of male employed household members 0.273(0.023)*** 0.778*** (0.043) Household size -0.108(0.011)*** -0.131*** (0.020) -0.115(0.015)*** No. of female household members below 14 years of age 0.268(0.044)*** 0.301*** (0.076) 0.962(0.034)*** No. of male household members below 14 years of age 0.298(0.044)*** 0.236** (0.074) 0.019(0.032) No. of pre-school children -0.273(0.046)*** -0.124 (0.081) -0.435(0.065)*** Help non-coresident siblings 0.319(0.041)*** 0.396*** (0.072) 0.314(0.055)*** Receive help from non-coresident siblings -0.126(0.040)*** -0.228** (0.071) -0.054(0.054) Rural 0.027(0.044) 0.005 (0.079) 0.004(0.058) Modified Mills Ratio 0.003 (0.049) 0.095 (0.096) -0.027 (0.067) N 17549 8270 9279 Uncensored N 6049 2709 3340

33

Working hours Full sample Males Females Modified Mills Ratio 0.003 (0.036) -0.041 (0.055) 0.030 (0.050) N 15675 7733 7942

Working hours

(restricted to working hours>10) Full sample Males Females Modified Mills Ratio -0.026 (0.036) -0.067 (0.051) 0.00005 (0.052) N 15419 7639 7780

Note: ***, ** and * refer to 1%, 5% and 10% levels of significance. Standard errors are in parentheses. Columns [1] and [2] refer to second stage estimates of female and male sample respectively and[3] and [4] refer to first stage estimates for female and male samples respectively. Second-stage dependent variable: I=1 if the respondent is working for at least 10 (normal) hours per week, I=0 otherwise. First-stage dependent variable: HH_50plus =1 if the respondent coresides with an elderly member.