Table S3.

The average treatment effects (ATE) of any material hardship on different health outcomes, Add Health wave I to wave IV

	ATE	Std. Err.	95% CI for ATE
Estimates from inverse probability weighting (IPW)†
Poor health	0.06∗∗∗	(0.01)	(0.04, 0.08)
Depression	0.08∗∗∗	(0.01)	(0.06, 0.11)
Sleep problem	0.04∗∗∗	(0.01)	(0.02, 0.06)
Suicidal thoughts	0.04∗∗∗	(0.01)	(0.03, 0.06)
Estimates from propensity score matching (PSM)‡
Poor health	0.05∗∗∗	(0.01)	(0.03, 0.07)
Depression	0.08∗∗∗	(0.01)	(0.06, 0.11)
Sleep problem	0.04∗∗∗	(0.01)	(0.02, 0.06)
Suicidal thoughts	0.04∗∗∗	(0.01)	(0.03, 0.06)

Each column and panel is from a different ATE estimate.

∗P < .05, ∗∗P < .01, ∗∗∗P < 0.001 (two-tailed tests).

95% confidence intervals in parentheses.

n = 13,313.

Std. Err. = standard error.

^†We use teffects commands in STATA 15 to estimate the average treatment effects using IPW approach. See Graham, Campos De Xavier Pinto [1] for details of the IPV methodology.

^‡We use teffects commands in STATA 15 to estimate the average treatment effects using PSM approach. The independent variables used in the propensity score matching include factors that are hypothesized to affect the probability of experiencing any material hardship and/or health outcomes. This constraint guides our choice of sociodemographic variables in the propensity scores, including age, sex, educational attainment, race/ethnicity, earnings, citizenship status, unemployment status, family size, and number of kids, receipt of public assistance, consistent depression (wave I through III), consistent poor health (wave I through III), family structure (wave I), and parent-child relationship quality (wave I) in calculating the propensity score.