Abstract
This study analyzes whether parental monetary investments in children stimulate support from sons and daughters. Using data from the China Health and Retirement Longitudinal Study, I find that parents invested twice more in sons than in daughters in terms of college education spending and marriage gifts value. Conversely, higher parental monetary investments are associated with higher increases in support from daughters than from sons in terms of best living proximity and most help with activities of daily living. Parental monetary investments in sons’ college education are positively associated with sons’ income and negatively associated with sons’ instrumental support, suggesting that such investments potentially generate higher opportunity costs of time for sons. Meanwhile, parental monetary investments in daughters’ marriage are positively associated with daughters’ instrumental support, which may occur through an income effect or through reciprocity from daughters. There is no conclusive evidence that parental monetary investments stimulate monetary and in-kind transfers from children, suggesting that material support possibly occurs out of social norm courtesy.
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Notes
This is also consistent with Rosenzweig and Zhang (2014)’s argument that financial support by the young to the old is not currently an important phenomenon in China.
Similarly, son targeting fertility behavior is a widespread phenomenon (Basu and De Jong 2010; Ebenstein 2010), while sons also tend to benefit from greater health investments in the form of longer breastfeeding duration, better nutrition, and higher medical expenditures (Barcellos et al. 2014; Jayachandran and Pande 2017; Rosenzweig and Schultz 1982).
Antman (2012), Brown (2006), Checkovich and Stern (2002), and Pezzin and Schone (1999) posited that children’s caregiving decisions are driven by altruistic considerations in Mexico and in the US. Cai et al. (2006) showed that monetary transfers from children are negatively associated with parents’ income while Oliveira (2016) posited that children’s coresidence and financial support to parents stem from altruism in China. Conversely, Oliveira (2016) found no evidence that children are exchanging financial transfers for child care services.
For example, one can have ui(ci, M, T) = ωi(ci) + γiv(M, T), where ωi(ci) is the felicity of consumption and γi is the weight on parental well-being, v. I do not consider paid help in the model since the use of paid help is very uncommon: Only 0.21% of children had parents who benefitted from any paid help in the data.
Contemporaneous inter vivos are not included in the main specification as such inter vivos may be simultaneously determined with intergenerational support. The main specification described below, may be interpreted as a reduced-form model. In particular, if intergenerational support is a function of inter vivos from parents and the latter are functions of parental and children’s characteristics, then one may replace inter vivos with parental and children’s characteristics, thereby resulting in a reduced-form empirical model for support.
Parents may also invest more in children who are more altruistic or in children with whom they feel more connected to. Such children may be more inclined to provide intergenerational support to parents, which may generate a positive correlation between parental investments and intergenerational support. If this is the case, then the remaining endogeneity issue may reinforce the estimated marginal returns to parental investments. The literature considering intergenerational support as functions of education attainment and marital status tends to bypass potential endogeneity issues (Cunningham et al. 2013; Lillard and Willis 1997; Raut and Tran 2005). Brown (2006), McGarry (2016) and Norton et al. (2014) use family fixed effects in their analyses of transfers as functions of children’s characteristics and contemporaneous or expected future transfers.
Given the other sample restrictions, this restriction was relevant for only 7.5% of families and 2.4% of children. The distribution of family size is reported in Online Appendix Figure A1. Sensitivity analyses from OLS and MLE models that include children from one child families yielded qualitatively similar results and conclusions to those reported below.
Best living proximity is highly correlated with coresidence and living in the same village or neighborhood (ρ = 0.88, p < 0.05). Sensitivity analyses using a dummy variable that takes a value of 1 if the child is coresident or lives close, and a value of 0 otherwise (i.e., ignoring parents’ preferred living arrangement for an elderly person) yielded qualitatively similar results and conclusions to those reported below.
This measure is combined with instrumental ADL since few children help with such activities. Approximately 70% (30%) of children were in families where the parents received the most help with ADL from only 1 child (2 or more children). The baseline analysis includes all children. Sensitivity analyses on the sample of children whose parents have an ADL limitation yielded qualitatively similar results to those reported below. This study also abstracts from hours of help since such data is missing for 47% of those who help with ADL. Such measurement issues are common in the caregiving literature (Checkovich and Stern 2002; Norton et al. 2014). Sensitivity analyses using the sum of a child’s and child-in-law’s hours of help as dependent variable, and additionally controlling for a missing value indicator, yielded qualitatively similar results to those reported below in OLS and FE models, while convergence was not achieved in the MLE model.
Given college education attainment and marital status, parental monetary investments may help children achieve higher income either through better education quality or through marrying a spouse with higher earnings capacity. Furthermore, children may be more inclined to reciprocate parental investments compared to self-sponsored children. The marginal support returns reported in the main specifications below, therefore, capture the associations between intergenerational support that is driven by parental monetary investments.
Local sex ratio is constructed at the community level, where the CHARLS sample contains 443 communities. Sensitivity analyses controlling for community level fixed effects instead of local sex ratio and province dummies, yielded quantitatively similar results in OLS models while convergence was not achieved in MLE models. The results are identical in FE models as any fixed family characteristics across children are differenced out.
Antman (2012) used the sum of siblings’ demographic characteristics as instruments to predict siblings’ support to parents, in an attempt to estimate how siblings’ support crowd-out a child’s support. As discussed in Section 2, the empirical models may be interpreted as reduced-formed models. In particular, if siblings’ supports are functions of parental investments in siblings, siblings characteristics, and parent characteristics, then by substituting such functions into the best response functions (1), one obtains a reduced-form model for support from the child. Sensitivity analyses without controlling for siblings variables yielded quantitatively very similar results in OLS and MLE models. Note that siblings variables are differenced out in family fixed effects models.
Whereas the CHARLS also contains information on hours of help with child care, such information was missing for 21.4% of those who received parental help with child care and therefore, not used in the empirical analysis. Parents provided on average 7.2 more hours of child care per week to sons than to daughters (p < 0.05).
Since sons within the same family may face similar bargaining power issues, I also attempted family fixed effects regressions on the sample of married sons in families with at least two sons (61.5% of the child sample). Once again, there were negative associations between marriage investments and material support from sons.
Online Appendix Table A2 reports the marginal returns excluding controls for children’s college education attainment and marital status. Parental monetary investments thus now proxy for attainment. The marginal effects are quantitatively very similar to those reported in Table 3. While there is some indication that sons may increase (iii) Any material support in response to parental education investments from OLS and MLE models (p < 0.05), the association is not statistically significant at the 5% level in the FE model. The associations between education investments and (iv) Amount of material support are also always statistically insignificant at the 5% level.
As income may be imprecisely measured, the results from child income models need to be interpreted with caution and may only provide some suggestive insights. An OLS regression of the missing income indicator on characteristics of parents and children reveals that child income is more likely to be missing when the family respondent parent is older, female, and less educated, and for female, married, and less educated children.
5% (1%) of daughters (sons-in-law) and 10% (6%) of sons (daughters-in-law) provide the most help with ADL. Separate estimation of Model (2) for an outcome capturing most help with ADL from children only (children-in-law only), yielded similar results to those reported for (ii) Most help with ADL from Table 2 across all (the OLS and FE) models. Convergence was not achieved for the outcome of most help with ADL from children-in-law only in the MLE model. The marginal effects were larger in magnitude for an outcome variable that sum most help with ADL across children and their spouse, with larger negative associations for sons and daughters-in-law and larger positive associations for daughters and sons-in-law. This suggests that children handle the bulk of such care towards their own parents and that their spouses may just be tagging along. In this case, the positive income effect is expected to prevail in daughters’ households while the negative substitution effect is expected to prevail in sons’ households.
It is also possible that higher parental monetary investments first induce sons to provide lower instrumental support (e.g., by making them become more selfish), which enables them to work longer hours and generate higher income. Caregiving is known to be negatively associated with labor supply (Schmitz and Westphal 2017; Skira 2015). On the other hand, the literature has argued that parental investments may encourage children to become more altruistic or exhibit greater reciprocity (Becker et al. 2016; Lillard and Willis 1997; Raut and Tran 2005). The results based on child income are interpreted in conjunction with the inferences based on investment types above: Education investments decreasing instrumental support from sons may be consistent with an increase in sons’ opportunity cost of time, which corroborates with an increase in sons’ income.
As mentioned in footnote 5, only 0.21% of children had parents who benefitted from any paid help in the sample. On the other hand, the average yearly expenditure on food was around 9,500 yuan for the parent household in CHARLS sample so that financial transfers from children may cover around 18% of such expenditure for parents. There were 256 million individuals (nearly one-fifth of China’s population) using online food ordering in 2016 and such online ordering services industry is estimated at 240 billion yuan, approximately $37 billion (Li 2017). Thus, there may still be substitution possibilities in components other than paid care.
Conditional on being positive, annual financial transfers average 1,700 yuan, which is less than 5% of children’s average annual income. As described in footnote 21, such amount may nevertheless be more significant from parents’ point of view.
Sensitivity analyses using a younger sample, inclusive of parents aged 50 and children aged 25 and above, yielded qualitatively similar results and conclusions to those reported above, suggesting that the results are robust across age groups.
The program was introduced at the regional level. Region fixed effects are controlled for in the OLS and MLE models while the FE models would difference out fixed parental characteristics among siblings, including parental pension take-up. The program was also found to have no effect on the probability and amount of financial transfers received by parents (Huang and Zhang 2016).
Cultural values may be persistent in other aspects than family care. For instance, there is evidence of persistence in son preference irrespective of a country’s level of development. Indeed, the literature has documented evidence of skewed sex ratios in favor of boys among US residents of Asian origin (Abrevaya 2009; Almond and Edlund 2008). Dahl and Moretti (2008) also find evidence that parents with a first born son in-utero are more likely get married and less likely to divorce using nationally representative US data.
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Acknowledgements
I would like to thank Pao-Li Chang, Le-Yu Chen, Michael Gechter, Fali Huang, Seonghoon Kim, Jing Li, Edward Norton, Aloysius Siow and seminar attendees at the ESPE and SEHO conferences, University of Bath, ADBI-AGI Workshop on Aging in Asia, RAND Economic Demography Workshop PAA, and Singapore Management University for insightful comments and suggestions. I am also grateful to Lin Li for sharing her China Census data. Support from the Shirin Fozdar Foundation and Singapore Management University is gratefully acknowledged. All mistakes remain my own.
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I, Christine Ho, declare that I have no relevant or material financial interests that relate to the researchdescribed in this paper.
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Ho, C. Child’s gender, parental monetary investments and care of elderly parents in China. Rev Econ Household 17, 741–774 (2019). https://doi.org/10.1007/s11150-018-9428-y
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DOI: https://doi.org/10.1007/s11150-018-9428-y