Table 5.

Comparison of Health Care Utilization of Migrant Parents between IMISs and. URBMIs after the Adjustment of Insurance Type

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
	IMISs vs. non-IMISs			IMISs vs. NCMSs			IMISs vs. URBMIs
	hospitalized in the past year(inpa)	hospitalized locally in the past year(local_inpa)	see a doctor locally with less serious diseases(less_serious_doctor)	hospitalized in the past year(inpa)	hospitalized locally in the past year(local_inpa)	see a doctor locally with less serious diseases(less_serious_doctor)	hospitalized in the past year(inpa)	hospitalized locally in the past year(local_inpa)	see a doctor locally with less serious diseases(less_serious_doctor)
imis	0.0147**	0.0244***	0.0431*	0.0109*	0.0195***	0.0436	0.0321***	0.0228	0.0125
	(0.00596)	(0.00696)	(0.0227)	(0.00646)	(0.00728)	(0.0310)	(0.00968)	(0.0525)	(0.0561)
the need of hospitalization(need_inpa)	√	√		√	√		√	√
Other control variables	√	√	√	√	√	√	√	√	√
SES	√	√	√	√	√	√	√	√	√
Fixed effects of origin provinces	√	√	√	√	√	√	√	√	√
Fixed effects of flow-in cities	√	√	√	√	√	√	√	√	√
N	7912	7912	7912	6885	6885	6885	1691	1691	1691

Note: Robust standard errors are reported in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. Other control variables include individual demographic characteristic, self-reported health status, having hypertension or diabetes, fitness time, having health examination or not in the past year. SES includes education, income, expenditure and immigration information. Same structure of dependent variable as in Table 2. The columns (1) – (3) and (4)– (6) have same structure as column (3), (6), and (8) in Table 2. The columns (7)– (9) have same structure as column (3), (6), and (8) in Table 4. In this regression, we redefine the potentially misreported insurance type, considering samples who are rural residents with agriculture hukou as IMISs