Table 4.

Multilevel probit regression models, average marginal effects, [95% CI]

	Tobacco1	Overweight2	Harmful alcohol use3	Unhealthy household behavior4
Rural household	Ref	Ref	Ref	Ref
Urban household	0.02 [.02–.03]	0.02 [.02–.03]	0.03 [.02–.03]	0.02 [.02–.03]
Age below 20 yrs (F)		Ref	Ref	Ref
20–35 yrs. (F)		0.17 [.16–.17]	0.09 [.08–.11]	0.06 [.04–.08]
above 35 yrs. (F)		0.25 [.25–.26]	0.11 [.10–.13]	0.07 [.05–.09]
Age below 20 yrs (M)	Ref			Ref
20–35 yrs. (M)	0.16 [.15–.16]			0.03 [−.03–.09]
above 35 yrs. (M)	0.17 [.16–.17]			0.06 [−.00–.12]
Illiterate (F)		Ref	Ref	Ref
Literate (F)		0.03 [.02–.03]	0.02 [.02–.03]	0.02 [.01–.03]
Illiterate (M)	Ref			Ref
Literate (M)	0.00 [−.00–.01]			0.01 [−.00–.01]
Education: Less then primary (F)		Ref	Ref	Ref
Secondary or higher (F)		−0.02 [−.02--.01]	− 0.01 [−.02--.00]	0.01
Education: Less then primary (M)	Ref			Ref
Secondary or higher (M)	−0.02 [−.03–.01]			− 0.01 [−.02 - -.00]
Occupation: none (F)		Ref	Ref	Ref
white-collar (F)		0.03 [.02–.03]	0.04 [.03–.05]	− 0.01 [−.02–.00]
blue-collar (F)		0.01 [.01–.02]	0.09 [.09–.10]	0.02 [.01–.02]
agriculture (F)		−0.03 [−.04--.03]	0.11 [.10–.11]	0.02 [.01–.02]
Occupation: none (M)	Ref			Ref
white-collar (M)	0.02 [.01–.02]			− 0.02 [−.04 - -.01]
blue-collar (M)	0.06 [.05–.06]			−0.00 [−.01–.01]
agriculture (M)	0.04 [.03–.04]			− 0.02 [−.03 - -.00]
Wealth index: very poor	Ref	Ref	Ref	Ref
poor	−0.00 [−.01–.00]	0.03 [.02–.03]	− 0.03 [−.04--.06]	0.00 [−.00–.01]
middle	− 0.00 [−.0–.00]	0.06 [.06–.07]	− 0.05 [−.06- -.05]	0.02 [.01–.02]
rich	− 0.02 [.03–.02]	0.11 [.11–.12]	− 0.07 [−.08 - -.07]	0.02 [.01–.03]
very rich	− 0.05 [−.06--.05]	0.17 [.17–.18]	− 0.11 [−.12- -.10]	0.00 [−.01–.01]
Household size	− 0.00 [−.00--.00]	− 0.00 [−.00--.00]	− 0.01 [−.01 - -.01]	− 0.01 [.01 - -.01]
Number of living children (F)		0.00 [.00–.00]	0.01 [.01–.01]	0.01 [.00–.01]
Number of living children (M)	0.00[.00–.00]			0.00 [−.00–.00]
Armenia	Ref	Ref	Ref	Ref
Albania	−0.17 [−.18--.15]	0.01 [−.09–.02]	.	.
Burundi	− 0.32 [−.33--.30]	0.14 [.13–.16]	0.10 [.08–.12]	− 0.24 [−.26 - -.21]
Cambodia	−0.17 [−.18--.15]	0.02 [.01–.04]	0.40 [.38–.42]	−0.08 [−.10 - -.06]
Chad	.	0.07 [.05–.08]	− 0.16 [−.18 -. -.14]	.
Congo	−0.25 [−.26--.23]	0.20 [.18–.21]	−0.02 [.04 - -.00]	− 0.34 [−.37 - -.31]
Egypt	.	0.42 [.41–.44]	.	.
Ethiopia	−0.38 [−.39--.36]	− 0.28 [−.29–.26]	−0.21 [−.23 - -.19]	−0.37 [−.39 - -.35]
Gambia	−0.23 [−.24--.21]	0.28 [−.27–.30]	Sample too small	−0.28 [−.31 - -.25]
India	−0.30 [−.31--.28]	−0.24 [−.26--.23]	−0.07 [−.08 - -.05]	−0.24 [−.26 - -.23]
Kenya	−0.29 [−.31--.28]	−0.12 [−.13--.10]	−0.08 [−.10 - -.06]	−0.21 [−.22 - -.19]
Malawi	−0.33 [−.34--.31]	0.34 [.32–.35]	−0.11 [−.13 - -.09]	−0.22 [−.25 - -.20]
Nigeria	−0.42 [−.43--.40]	−0.13 [−.14--.11]	−0.26 [−.28 - -.25]	−0.31 [−.33 - -.29]
Rwanda	−0.37 [.39--.35]	0.19 [.18–.21]	0.05 [.03–.08]	−0.22 [.25 - -.20]
Sierra Leone	−0.19 [−.20–.18]	0.22 [.21–.24]	−0.28 [−.31 - -.26]	−0.24 [−.26 - -.22]
South Africa	−0.12 [−.13--.10]	0.46 [.44–.48]	0.04 [.02–.06]	−0.07 [−.10 - -.04]
Tanzania	−0.32 [−.34--.31]	−0.12 [−.13--.10]	−0.14 [−.16 - -.12]	−0.22 [−.25 - -.19]
Tajikistan	.	−0.06[−.08--.05]	− 0.19 [−.21 - -.17]	.
Timor-Leste	−0.00 [−.02–.01]	−0.38[−.39--.36]	−0.04 [.06 - -.02]	− 0.26 [−.28 - -.23]
Togo	− 0.36 [−.38--.35]	0.22 [.20–.23]	−0.14 [−.16 - -.11]	−0.25 [−.27 - -.22]
Zambia	−0.25 [−.27--.24]	−0.18 [−.20--.17]	0.04 [.02–.06]	−0.14 [−.16 - -.12]
Zimbabwe	−0.26[−.27--.24]	−0.07[−.08--.05]	−0.00 [−.02–.01]	−0.13 [−.15 - -.11]
N	245,835	443,528	172,450	90,578

¹: Men’s dataset; ²: Women’s dataset; ³: Women’s dataset (reporting on partner’s alcohol use); ⁴: Couples’ dataset

Italian: significant at p < 0.05

F: reported in women’s dataset

M: reported in men’s dataset