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Do coresidency and financial transfers from the children reduce the need for elderly parents to works in developing countries?

Abstract

Do elderly parents use coresidence with or financial transfers from children to reduce their own labour supply in old age? This paper is one of only a few studies that seeks to formally model elderly labour supply in the context of a developing country while taking into account coresidency with and financial transfers from children. We find little evidence that support from children—either through transfers or coresidency—substitutes for elderly parents’ need to work. Thus, as in developed countries, there is a role for public policy to enhance the welfare of the elderly population.

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Fig. 1

Notes

  1. The impact of modernization on support for the elderly is a point of empirical debate, for example, see Frankenberg and Kuhn (2003) and Beard and Cartmill (2003, unpublished data).

  2. There is a literature that assesses the impact of familial transfers on labour supply in developed countries. For example, Joulfaian and Wilhelm (1994) and Holtz-Eakin et al. (1993) study the effect of inheritances on labour supply, whereas Rosenzweig and Wolpin (1993) model the effect of parental support—through both coresidence and financial transfers—on the human capital investment and labour supply decisions of young, adult sons.

  3. Niehof (1995), although not dealing directly with labour supply, presents an interesting overview of the experiences of elderly Indonesians.

  4. Special issues of the Asia-Pacific Population Journal were devoted to coresidency in September 1992 and December 1997. These papers are largely descriptive. See also Hermalin (2002), who reports the results of a 10-year study of ageing in Taiwan, Singapore, Thailand and the Philippines.

  5. See Hoerger et al. (1996) and Pezzin and Schone (1999) for papers that examine elderly living arrangements in the USA.

  6. See Cox and Jimenez (1992) and Jensen (2004) for example. Khemani (1999) takes a different approach and examines whether intergenerational transfers in Indonesia are explained by bargaining between husbands and wives over how much to transfer to their respective parents.

  7. Lillard and Willis (1997) provide more extensive descriptions of each of these motives.

  8. Results from developed countries have been just as indecisive. For example, Cox (1987) and Cox and Rank (1992) reject altruism on the basis that transfers in the USA are positively correlated with recipient’s incomes, whereas McGarry and Schoeni (1995) and Altonji et al. (1997) find the opposite correlation and conclude in favour of altruism. There have also been attempts to examine transfers within households (see Kochar 1999; Pezzin and Schone (1997).

  9. In a previous paper (Cameron and Cobb-Clark 2002) we modeled labour and transfers jointly but treated coresidency as being exogenous.

  10. Most South east Asian countries are like Indonesia in this respect (Friedman et al. 2003).

  11. We have no direct way of controlling for ethnicity given our data. We initially included provincial dummies in the estimating equations as a proxy for ethnicity but found that they were statistically insignificant. This is not particularly surprising because there are normally several ethnic groups within a single province.

  12. A second round of the survey was conducted in 1997, but the data on labour supply were not available at the time of writing. A third round was collected in 2000.

  13. In 1993, the average life expectancy in Indonesia was 63 (World Bank 1995). Each household has a maximum of four adult respondents. Where this maximum was binding, the selection rules resulted in a relatively high probability that the elderly household members would be chosen. The final sample consists of elderly individuals who can answer the questions about their non-coresiding children themselves. Our sample may thus underrepresent the elderly who were particularly frail or disabled.

  14. The IFLS asks people about the hours they normally worked on their primary job and their secondary job. We summed these two figures to arrive at the total hours normally worked. A small but not insignificant percentage of the sample reported working long hours on both jobs such that the total hours worked was not feasible. As a result normal hours worked was top-coded at 84 h/week. We experimented with allowing for this upper censoring in the estimation and found that it made little difference. The MLE results below control for lower censoring only.

  15. Respondents are asked the monetary value of help received in the form of money, tuition, health care, food or other goods.

  16. Indonesia had 27 provinces in 1993.

  17. Consistent with standard labour supply models, labour market conditions influence the labour supply decision through market wages, that is, through the return to productivity-related characteristics (like education) that are captured in ZP. In Indonesia, demand-side constraints are likely to be relatively unimportant because most elderly individuals are not employees but are engaged in some form of self-employment.

  18. The IFLS also provides information on transfers to children from parents. These are quantitatively much less important. We experimented with subtracting this amount from transfers from children and using a net measure of transfers in the estimation. It, however, seems that the motivations for these two types of transfers differ significantly. Using the net measure of transfers instead of the gross measure significantly reduced the predictive power of the transfers equation. We hence elected to use gross transfers to parents as our measure of transfers. Lee et al. (1994) similarly focus just on transfers flowing towards parents, and Frankenberg et al. (2002c) recognise that the determinant of these two types of transfers differ and estimate separate equations for provision and receipt of transfers.

  19. We considered a number of alternative ways of characterizing children and found marital status and education to be the most appropriate for our analysis. We do not specifically consider the age of the children because the “children” of our sample of elderly Indonesians are in fact themselves adults. Only 2.2% of the sample have children younger than 18, and only 27.6% have children between the ages of 18 and 25. Consequently, the age of these children is unlikely to be the most important factor driving parental labour supply. We originally also controlled for the gender of children but found it to be insignificant. This is not so surprising because unlike South Asia, for example, gender roles are less well defined in Indonesia and elsewhere in South east Asia.

  20. In models of developed countries, nursing home care might be included as an additional potential living arrangement, for example, as in Pezzin and Schone (1999). Such care is very rarely available in Indonesia and so is not considered here.

  21. Our approach is not dissimilar to that of Pezzin and Schone (1999), who model elderly living arrangements, daughters’ provision of informal care to elderly parents and daughters’ labour supply in the context of the USA. They similarly include a proxy for the costs of moving between residency states in their residency equation as a means of identifying their system of equations.

  22. Initial estimation of the full model failed to converge. The convergence problem appeared to be due to the likelihood function being relatively flat around the optimum owing to the large number of discrete variables in the model. We therefore estimated the parameters in the coresidency equation in a separate step, plugged these parameters into the full likelihood function shown in the Appendix 2 and maximised it over the remaining parameters. Whereas this two-step process results in some loss of efficiency, the resulting estimates are consistent.

  23. Individuals are classified as disabled if they report having difficulty standing from sitting, dressing or going to the bathroom by themselves. Mete and Schultz (2002) similarly found that elderly labour supply in Taiwan was responsive to health status.

  24. Interactions between children’s marital status and gender were insignificant. We also experimented with including the number of grandchildren living in the household of coresiding elderly parents in the labour supply equations. (This information is available, whereas the number of children of non-coresiding children is not.) We had hypothesised that elderly women might be involved in child care and so less likely to work. However, the number of grandchildren coresiding was consistently insignificant.

  25. For the purpose of comparison, results assuming these forms of support are unrelated are presented in Appendix 1 Tables 8 and 9. The marginal effects differ slightly across the jointly and independently estimated equations, as does the significance of some of the variables—most noticeably, transfers are strongly significant in the non-coresiding women’s labour supply equation (t = 3.37) but only marginally so once we allow for their endogeneity (t = −1.91). The effect is quantitatively small in both cases though. The qualitative results of both sets of results are largely the same.

  26. The correlation between the error in the transfer equation and coresidency equation is also insignificant in most cases for coresiding men, although it is significant and negative. Hence, men whose unobservable traits make them more likely to coreside are less likely to receive transfers. It is not unusual for Indonesian men to marry much younger women and to have relatively young children even when they are over the age of 60. Thus, this result may be explained by men who live with a young spouse who works being less likely to receive transfers and more likely to be coresiding with one of their youngish children.

  27. Similarly, children are defined to be coresiding if they live with the parent and non-coresiding if not. A non-coresiding child can thus have a coresiding parent. This simply implies that the parent lives with one of the child’s siblings.

  28. We tried including a quadratic in age in the model but it was insignificant.

  29. The IFLS data tell us who owns the home in which the household lives, but this does not allow us to accurately establish who is living with whom. For example, a parent at age 85 may no longer play an active role in household decisions (and so be dependent on his or her children in this sense) but may legally remain the owner of the home. Even if we knew a lot more about the household, this would be difficult to ascertain because responsibility is likely to gradually shift from the parent to the child over time. It is also possible that—given the nature of the data—we are capturing the effects of birth cohorts rather than aging. However, Frankenberg et al. (1999) used panel data for Indonesia and similarly found age to be negatively related to the transition to coresidency.

  30. The Indonesian currency is the Rupiah. In 1993, US\$1 bought approximately Rp2,500. We treat assets as a predetermined variable. It can be argued that assets are actually endogenous because the parent may run them down if s/he does not receive income support from other sources. However, we examined the asset data and found no evidence of asset values changing systematically, either increasing or decreasing, with age older than 60. We also estimated the entire system of equations without the inclusion of the asset variable and found none of the other parameters to be affected by its presence. We chose to present the results that include the asset variable because theoretically, wealth can play an important role in the choices elderly individuals make regarding their income support.

  31. Given the cross-sectional nature of our data, this pattern may reflect differences across birth cohorts rather than the effects of ageing per se.

  32. Ideally, we would have also controlled for the number of children that each of the non-coresiding children have (that is, grandchildren of the elderly individual) because they may constitute a competing demand on the non-coresiding child’s resources. However, the IFLS does not provide these data for all non-coresiding children.

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Acknowledgements

We thank Patricia Apps, Robert Breunig, Denise Doiron, Mardi Dungey, Thomas Crossley, Bo Honore, Cordelia Reimers and two anonymous referees for helpful comments. This research was funded by Australian Research Council grant no. S79813009.

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Correspondence to Deborah Cobb-Clark.

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Responsible editor: Junsen Zhang

Appendices

Appendix 1

Table 6 Variable definitions
Table 7 Mean parental and child characteristics by gender and coresidency
Table 8 Transfers from non-coresiding children based on single-equation model (Tobit marginal effects and t statistics)
Table 9 Determinants of weekly normal hours of work based on single-equation model (marginal effects and t statistics)

Appendix 2 Joint maximum likelihood estimation of the coresidency, transfers and labour supply equations

The estimation is conducted separately by coresidency status and gender. We present the likelihood function for a non-coresiding mother or father below. The procedure for coresiding parents is analogous to this.

We begin by simplifying the notation used in Eqs. 16 in the text. This will make it easier to write out the likelihood expressions. The simplified coresidency equation is:

$$\begin{array}{*{20}c} {{C_{i} = 1{\left( {\eta _{0} + \eta _{1} Z^{P}_{i} + \eta _{2} Z^{C}_{i} + \eta _{3} H_{i} + \nu _{i} > 0} \right)}}} \\ {{ = 1{\left( {\eta Z_{i} + \nu _{i} > 0} \right)}}} \\ \end{array} $$
(7)

where 1( ) is an indicator function that equals 1 if the expression in the brackets is true and 0 otherwise, and Z i is the column vector \( {\left[ {1,{\mathbf{Z}}_{i} ^{{\text{P}}} ,{\mathbf{Z}}_{i} ^{{\text{C}}} ,H_{i} } \right]} \).

For transfers, we have:

$$ \begin{array}{*{20}c} {{\text{TR}}_{i} }{ = \max {\left( {\pi _{{0n}} + \pi _{{1n}} {\mathbf{Z}}_{i} ^{{\text{P}}} + \pi _{{2n}} {\mathbf{Z}}_{i} ^{{{\text{NC}}}} + u_{{1i}} ,0} \right)}} \\ {}{ = \max {\left( {\pi {\mathbf{X}}_{i} + u_{{1i}} ,0} \right)}} \\ \end{array} , $$
(8)

where X i is the column vector \( [1,{\text{ }}{\mathbf{Z}}_{i} ^{{\text{P}}} {\text{, }}{\mathbf{Z}}_{i} ^{{{\text{NC}}}} ]. \) Finally, labour supply is given by:

$$ \begin{array}{*{20}c} {LS_{i} ^{P} }{ = \max {\left( {\beta _{{0n}} + \beta _{{1n}} {\mathbf{Z}}_{i} ^{{\text{P}}} + \gamma _{{1n}} {\text{TR}}_{i} + \varepsilon _{{1i}} ,0} \right)}} \\ {}{ = \max {\left( {\beta {\mathbf{W}}_{i} + \gamma {\text{TR}}_{i} + \varepsilon _{{1i}} ,0} \right)}} \\ \end{array} , $$
(9)

where W i is the column vector \( {\left[ {1,{\mathbf{Z}}_{i} ^{{\text{P}}} } \right]}. \)

We assume that the error terms are jointly normally distributed such that:

$${\left( {\begin{array}{*{20}c} {{\nu _{i} }} \\ {{u_{{1i}} }} \\ {{\varepsilon _{{1i}} }} \\ \end{array} } \right)} \sim N{\left( {\begin{array}{*{20}c} {0\;1}{\rho _{{\nu u_{1} }} \sigma _{{u_{1} }} }{\rho _{{\nu \varepsilon _{1} }} \sigma _{{\varepsilon _{1} }} } \\ 0{}{\rho _{{u_{1} \varepsilon _{1} }} \sigma _{{u_{1} }} \sigma _{{\varepsilon _{1} }} } \\ {0,}{}{\sigma ^{2}_{{\varepsilon _{1} }} } \\ \end{array} } \right)}.$$

Deriving the likelihood function

The elderly non-coresiding individual can be in one of four states. Below, we list the states and the expression for the associated probability of being in each state.

Coresiding (C i = 1)

The probability associated with being in this state is written as:

$$ \begin{array}{*{20}c} {L_{{1i}} }{ = \Pr {\left( {C_{i} = 1} \right)}} \\ {}{ = \Pr {\left( {\nu _{i} > - \eta z_{i} } \right)}} \\ {}{ = 1 - \Phi {\left( { - \eta z_{i} } \right)}} \\ \end{array} , $$

where Φ is the normal cumulative distribution function. Note that we are following the convention of using uppercase letters to represent the random variables and lowercase to represent the realization of the variables.

Non-coresiding, receiving positive transfers and having positive labour supply

$$ {\left( {C_{i} = 0,{\text{TR}}_{i} > 0,LS^{P} _{i} > 0} \right)} $$
$$ \begin{array}{*{20}c} {L_{{2i}} = \Pr {\left( {C_{i} = 0,{\text{TR}}_{i} = tr_{i} ,LS_{i} ^{P} = ls_{i} } \right)}} \\ { = \Pr {\left( {{\text{TR}}_{i} = tr_{i} ,LS_{i} ^{P} = ls_{i} } \right)} \times \Pr {\left( {C_{i} = 0\left| {{\text{TR}}_{i} } \right. = tr_{i} ,LS_{i} ^{P} = ls_{i} } \right)}} \\ { = \Pr {\left( {u_{{1i}} = tr_{i} - \pi x_{i} ,\varepsilon _{{1i}} = ls_{i} - \beta w_{i} - \gamma tr_{i} } \right)} \times \Pr {\left( {\nu _{i} < - \eta z_{i} \left| {u_{{1i}} } \right. = tr_{i} - \pi x_{i} ,\varepsilon _{{1i}} = ls_{i} - \beta w_{i} - \gamma tr_{i} } \right)}} \\ {\varphi _{2} {\left( {tr_{i} - \pi x_{i} ,ls_{i} - \beta w_{i} - \gamma tr_{i} } \right)} \times \Phi {\left( { - \eta z_{i} \left| {tr_{i} } \right. - \pi x_{i} ,ls_{i} - \beta w_{i} - \gamma tr_{i} } \right)}} \\ \end{array} , $$

where ϕ 2 is the bivariate normal density function.

Non-coresiding, receiving positive transfers and not working

$$ {\left( {C_{i} = 0,\,{\text{TR}}_{i} > 0,\,LS^{P} _{i} = 0} \right)} $$
$$ \begin{array}{*{20}c} {L_{{3i}} = \Pr {\left( {C_{i} = 0,LS_{i} ^{P} = 0,{\text{TR}}_{i} = tr_{i} } \right)}} \\ { = \Pr {\left( {{\text{TR}}_{i} = tr_{i} } \right)} \times \Pr {\left( {LS_{i} ^{P} = 0,C_{i} = 0\left| {{\text{TR}}_{i} } \right. = tr_{i} } \right)}} \\ { = \Pr {\left( {u_{{1i}} = tr_{i} - \pi x_{i} } \right)} \times \Pr {\left( {\nu _{i} < - \eta z_{i} ,\varepsilon _{{1i}} < - \beta w_{i} - \gamma tr\left| u \right._{{1i}} = tr_{i} - \pi x_{i} } \right)}} \\ {\varphi _{2} {\left( {tr_{i} - \pi x_{i} } \right)} \times \Phi _{2} {\left( { - \eta z_{i} , - \beta w_{i} - \gamma tr_{i} \left| {tr_{i} - \pi x_{i} } \right.} \right)}} \\ \end{array} , $$

where φ is the normal probability density function, and Φ 2 is the bivariate normal cumulative distribution function.

Non-coresiding, receiving no transfers and working

$$ {\left( {C_{i} = 0,\;{\text{TR}}_{i} = 0,LS^{P} _{i} > 0} \right)} $$
$$\begin{array}{*{20}c} {L_{{4i}} = \Pr {\left( {C_{i} = 0,\operatorname{TR} _{i} = 0,LS_{i} ^{P} = ls_{i} } \right)}} \\ { = \Pr {\left( {LS_{i} ^{P} = ls_{i} } \right)}.\Pr {\left( {{\text{TR}}_{i} = 0,C_{i} = 0\left| {LS_{i} ^{P} = Is_{i} } \right.} \right)}} \\ { = \Pr {\left( {u_{i} = ls_{i} - \beta w_{i} - \gamma tr_{i} } \right)} \times \Pr {\left( {u_{{1i}} < - \pi x_{i} ,\nu _{i} < - \eta z_{i} ,\left| {\varepsilon _{{1i}} = ls_{i} - \beta w_{i} - \gamma tr} \right._{i} } \right)}} \\ { = \phi {\left( {ls_{i} - \beta w_{i} - \gamma tr_{i} } \right)} \times \Phi _{B} {\left( { - \pi x_{i} , - \eta z_{i} \left| {ls_{i} - \beta w_{i} - \gamma tr_{i} } \right.} \right)}} \\ \end{array} .$$

Non-coresiding, receiving no transfers and not working

$$ {\left( {C_{i} = 0,\,LS^{P} _{i} = 0,\,{\text{TR}}_{i} = 0} \right)} $$
$$ \begin{array}{*{20}c} {L_{{5i}} = \Pr {\left( {C_{i} = 0,LS_{i} ^{P} = 0,{\text{TR}}_{i} = 0} \right)}} \\ { = \Pr {\left( {\nu _{i} < - \eta z_{i} ,\varepsilon _{{1i}} < - \beta w_{i} - \gamma tr_{i} ,u_{{1i}} < - \pi x_{i} } \right)}} \\ { = \Phi _{3} {\left( { - \eta z_{i} , - \beta w_{i} - \gamma tr_{i} , - \pi x_{i} } \right)}} \\ \end{array} , $$

where Φ 3 is the trivariate normal cumulative distribution function.

Log likelihood function

The log likelihood is thus written as:

$$\log L_{i} = 1{\left( {C_{i} = 1} \right)} \times \log L_{{1i}} + 1{\left( {C_{i} = 0,TR_{i} > LS_{i} > 0} \right)} \times \log L_{{2i}} + 1{\left( {C_{i} = 0,TR_{i} > 0,LS_{i} = 0} \right)} \times \log L_{{3i}} + 1{\left( {C_{i} = 0,TR_{i} = 0,LS_{i} > 0} \right)}.\log L_{{4i}} + 1{\left( {C_{i} = 0,TR_{i} = 0,LS_{i} = 0} \right)}.\log L_{{5i}} .$$

The analogous states for the coresiding case are the probabilities associated with (1) non-coresiding; (2) coresiding, receiving positive transfers and working; (3) coresiding, receiving positive transfers and not working; (4) coresiding, receiving no transfers and working; and (5) coresiding, receiving no transfers and not working. The set of explanatory variables in the transfers and labour supply equations differ in the coresiding case, as shown in Eqs. 14.

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Cameron, L.A., Cobb-Clark, D. Do coresidency and financial transfers from the children reduce the need for elderly parents to works in developing countries?. J Popul Econ 21, 1007–1033 (2008). https://doi.org/10.1007/s00148-006-0105-8

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Keywords

  • Intergenerational transfers
  • Old-age support
  • Elderly labour supply

JEL Classification

  • J226
  • J22
  • J14