The Effect of Caregiving from Children on Health Status of the Elderly: Protection or Selection? Zhang Zhen Max Planck Institute for Demographic Research.

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Presentation transcript:

The Effect of Caregiving from Children on Health Status of the Elderly: Protection or Selection? Zhang Zhen Max Planck Institute for Demographic Research Rostock, 2004

Introduction Protection of caregiving –Caregiving improves the health/survival status of the elderly Selection of caregiving –The elderly in poor health are more likely to receive caregiving Caregiving from childrenHealth/survival status Question: if such two effects are mixed in, e.g., a regression model, what will the estimation of the effect of caregiving on health status of the elderly be?

Introduction Unobserved heterogeneity: –Were the selection or unobserved heterogeneity ignored, the estimation will be biased to some extent or even completely wrong Initial: selection process for mortality (Vaupel, Manton & Stallard, 1979) More extensive: –Fertility (Larsen & Vaupel, 1993) –Marriage and divorce (Lillard, Brien & Waite, 1995) –Marital status and mortality (Lillard & Panis, 1996) –Mortality and health service use (Panis & Lillard, 1994) More recently: –Caregiving from children significantly negatively influences the survival status of the oldest old without control of health conditions of the elderly (Zhang, 2002)

Introduction Joint model –Simultaneously investigating two effects of caregiving with consideration of unobserved heterogeneity Dyadic approach –Theoretical progress: Aging of individual → Aging in family (Allen, Blieszner & Roberto, 2000) –Chinese context: Norms of filial piety (Bartlett & Phillips 1997; Lee & Xiao, 1998 ), which were even written into laws and constitution (Wu,1994) “need-based support” (Lee & Xiao, 1998)

Methods Sample: dyadic data –The 3rd wave of CLHLS in 2002 –Survey for Family Dynamics of the Elderly’s Children (SFDC) in 2002 conducted at the same time as the 3rd wave of CLHLS An adult child age of each older respondent age 65+ in this sample pool was randomly chosen by specific composition of sex, age and rural/urban residence of children. Dyads valid for this study: 4364 → 3929 Out of age range: for the elderly; for adult child Missing value in vital variable (e.g. SRH of the elderly)

Methods Model –Ordinal Probit model for SRH of older parent y*: a latent propensity dependent on independent variables, and y : SRH reported by older parent; X p : sociodemographic characteristics of older parent; H p : health condition indicators of older parent ; C: caregiving to older parent reported by the adult child; ε: residual term for SRH of the parent.

Methods Model –Probit model for caregiving from child p*: a latent propensity dependent on independent variables, and p: an observed indicator variable; X c : sociodemographic characteristics of child; H p : health condition indicators of older parent; δ: residual term for occurrence of caregiving from child.

Methods SRH of ParentCaregiving from Child Parent ’ s Information SRH SexX AgeX RaceX Place of residenceX Living arrangementX Marital StatusX IADLXX ADLXX Suffering from serious diseaseXX Child ’ s Information SexX AgeX Urban/rural residenceX Coresident with parentX CaregivingX Table 1 Model Specifications

Methods The heterogeneity components for two models above are assumed to be jointly normally distributed The critical test of selection is whether σ εδ =0. Software package: aML 2.0 (Lillard and Panis, 2003)

Results Older Parent (n=3929)%Adult Child (n=3929)% SRHPoor13.4CaregivingNo58.5 Fair38.5Yes41.5 Good35.5SexFemale30.5 Very good12.6Male69.5 SexFemale53.0Age Male Age ResidenceRural Urban40.8 EthicMinorities9.1Coresident with parentYes43.6 Han90.9No56.4 ResidenceUrban46.3 Rural54.7 Marital StatusUnmarried63.5 Married36.5 Living arrangementAlone14.2 With families85.8 ADLIndependent79.0 Dependent21.0 IADLIndependent38.8 Dependent61.2 Serious diseaseNo82.0 At lease once18.0 Table 2 Sample Characteristics

SRHCaregivingJoint Model τ1τ *** *** τ2τ ** τ3τ *** *** Male (female=0) * ** Age (65-79=0) *** ** Han (minorities=0) * * Rural (urban=0) Married (unmarried=0) Live with families (alone=0) ADL dependent (independent=0) *** *** IADL dependent (independent=0) *** *** 1+ serious disease (no=0) *** *** Caregiving (no=0) ** *** Intercept *** ** Male (female=0) ** *** Age(35-44=0) *** *** *** * Rural (urban=0) *** Not-coresident with parent(yes=0) *** *** ADL dependent (independent=0) *** *** IADL dependent (independent=0) *** *** 1+ serious disease (no=0) *** *** σεσε *** *** σδσδ *** *** ρ εδ *** ln-L * p<.05; **p<.01; *** p<.001 Table 3 Joint Model vs. Separate Models for SRH and Caregiving

SRHCaregivingJoint Model τ1τ *** *** τ2τ ** τ3τ *** *** Male (female=0) * ** Age (65-79=0) *** ** Han (minorities=0) * * Rural (urban=0) Married (unmarried=0) Live with families (alone=0) ADL dependent (independent=0) *** *** IADL dependent (independent=0) *** *** 1+ serious disease (no=0) *** *** Caregiving (no=0) ** *** Intercept *** ** Male (female=0) ** *** Age(35-44=0) *** *** *** * Rural (urban=0) *** Not-coresident with parent(yes=0) *** *** ADL dependent (independent=0) *** *** IADL dependent (independent=0) *** *** 1+ serious disease (no=0) *** *** σεσε *** *** σδσδ *** *** ρ εδ *** ln-L * p<.05; **p<.01; *** p<.001 Table 3 Joint Model vs. Separate Models for SRH and Caregiving

SRHCaregivingJoint Model τ1τ *** *** τ2τ ** τ3τ *** *** Male (female=0) * ** Age (65-79=0) *** ** Han (minorities=0) * * Rural (urban=0) Married (unmarried=0) Live with families (alone=0) ADL dependent (independent=0) *** *** IADL dependent (independent=0) *** *** 1+ serious disease (no=0) *** *** Caregiving (no=0) ** *** Intercept *** ** Male (female=0) ** *** Age(35-44=0) *** *** *** * Rural (urban=0) *** Not-coresident with parent(yes=0) *** *** ADL dependent (independent=0) *** *** IADL dependent (independent=0) *** *** 1+ serious disease (no=0) *** *** σεσε *** *** σδσδ *** *** ρ εδ *** ln-L * p<.05; **p<.01; *** p<.001 Table 3 Joint Model vs. Separate Models for SRH and Caregiving

SRHCaregivingJoint Model τ1τ *** *** τ2τ ** τ3τ *** *** Male (female=0) * ** Age (65-79=0) *** ** Han (minorities=0) * * Rural (urban=0) Married (unmarried=0) Live with families (alone=0) ADL dependent (independent=0) *** *** IADL dependent (independent=0) *** *** 1+ serious disease (no=0) *** *** Caregiving (no=0) ** *** Intercept *** ** Male (female=0) ** *** Age(35-44=0) *** *** *** * Rural (urban=0) *** Not-coresident with parent(yes=0) *** *** ADL dependent (independent=0) *** *** IADL dependent (independent=0) *** *** 1+ serious disease (no=0) *** *** σεσε *** *** σδσδ *** *** ρ εδ *** ln-L * p<.05; **p<.01; *** p<.001 Table 3 Joint Model vs. Separate Models for SRH and Caregiving

Conclusions There is the selection for caregiving If the selection for caregiving is ignored, the protective effect of caregiving to the health status of the elderly will be misestimated heavily. When the selection for caregiving is “splitted out”, the protective impact of caregiving on the health of the elderly is shown up.

Acknowledgment I want to acknowledge my debt to Prof. Jan Hoem for his helpful encouragement for this study, and to Prof. Zeng Yi, Prof. Liu Yuzhi, Prof. Lu Jiehua, Prof. Zheng Zhenzhen, and Prof. Zhou Yun for their comments. I also wish to thank Max Planck Institute for Demographic Research for the scholarship to support this study which became one of main parts of my doctoral dissertation.