Table 4.

Growth curve models predicting the effect of children’s education on IADL and ADL among older adults by hukou, China Health and Retirement Longitudinal Study, 2011 to 2018.

Variable	On IADL				On ADL
	Model 1 (Urban)		Model 2 (Rural)		Model 3 (Urban)		Model 4 (Rural)
	Coef.	SE	Coef.	SE	Coef.	SE	Coef.	SE
Child schooling years	-0.041+	0.025	-0.073***	0.013	-0.030+	0.016	-0.038***	0.008
Age (centered on the mean)	0.097*	0.040	0.203***	0.017	0.066*	0.029	0.087***	0.012
Child schooling*age	0.002	0.003	-0.004*	0.002	-0.001	0.002	-0.003*	0.001
Education	-0.077***	0.019	-0.041***	0.014	-0.016	0.011	-0.012	0.008
Male (ref., Female)	-0.661***	0.150	-1.056***	0.089	-0.132	0.088	-0.225***	0.052
Urban (ref., Rural)	/	/	/	/	/	/	/	/
Married (ref., widowed/separated)	0.072	0.155	-0.019	0.087	0.059	0.099	-0.007	0.055
Number of living children	0.133*	0.053	0.065*	0.026	0.038	0.033	0.033*	0.016
Logged household expenditure per capital	0.128***	0.040	0.090***	0.022	0.055*	0.026	0.084***	0.014
Random effects-variance component
Level 1: within person	3.042***	0.097	4.897***	0.075	1.357***	0.044	2.153***	0.033
Level 2: in intercept	3.441***	0.237	4.820***	0.163	1.496***	0.111	1.679***	0.065
Level 2: in linear growth rate	0.019***	0.005	0.018***	0.003	0.016***	0.002	0.015***	0.001
Constant	1.740***	0.512	3.145***	0.248	0.456***	0.335	0.516***	0.159
Residual correlation between intercept and linear growth rate	0.165***	0.021	0.218***	0.015	0.133***	0.012	0.118***	0.007
AIC	14,578		66,774		11,903		55,059
BIC	14,657		66,872		11,983		55,157
ICC	0.502		0.456		0.504		0.405
n of persons	823		3449		823		3449
n of person-year observations	3292		13,796		3292		13,796

AIC = Akaike information criterion, BIC = Bayesian information criterion, the smaller the better; ref = reference; Coef.=coefficient; S.E.=standard error. p < 0.1 ⁺, p < 0.05 *, p < 0.01 **, p < 0.001 *** (two tailed tests).