Table 5.

The effect of boarding on the mental health of students from different groups.

$\mathit{D}\mathit{e}\mathit{p}\mathit{e}\mathit{n}\mathit{d}\mathit{e}\mathit{n}\mathit{t} \mathit{v}\mathit{a}\mathit{r}\mathit{i}\mathit{a}\mathit{b}\mathit{l}\mathit{e}\mathit{:}\Delta \mathit{M}\mathit{H}{\mathit{T}}_{\mathit{i}\mathit{s}}=\mathit{M}\mathit{H}{\mathit{T}}_{\mathit{i}\mathit{s},\mathit{e}\mathit{n}\mathit{d}\mathit{l}\mathit{i}\mathit{n}\mathit{e}} - \mathit{M}\mathit{H}{\mathit{T}}_{\mathit{i}\mathit{s},\mathit{b}\mathit{a}\mathit{s}\mathit{e}\mathit{l}\mathit{i}\mathit{n}\mathit{e}}$	(1)	(2)	(3)
	ATT a	SE	t-Value
Grade	−0.19 **	(0.09)	−2.13
(1) The effect of boarding for fourth-grade students	−0.13 *	(0.07)	−1.89
(2) The effect of boarding for fifth-grade students	0.13 **	(0.06)	2.15
If parents both migrate for work	0.31 **	(0.13)	2.4
(3) The effect of boarding for families with both parents migrating for work	0.30 **	(0.15)	1.97
(4) The effect of at least one parent not migrating for work and staying at home	−0.03	(0.05)	−0.75
If students are left-behind children (LBC) (5) The effect of boarding for LBC	0.29 **	(0.13)	2.16
N	16,685	16,685	16,685

Note: *** p < 0.01, ** p < 0.05, * p < 0.1 Standard errors were calibrated by bootstrap (100 times). The table shows the regression results of the heterogeneity analysis for different groups. Each regression equation controlled the personal characteristics of the students (including gender, age, grade, myopia, standardized mathematics scores of the students at baseline, and the distance from the school to the county) and family background (including the natural logarithm of family finance, the educational level of parents, and whether parents migrate for work). ^a ATT, average treatment effect for the treatment, indicating the real effect of boarding on students’ mental health. Data source: Authors’ survey.