Skip to main content
. Author manuscript; available in PMC: 2022 Mar 1.
Published in final edited form as: Stud Fam Plann. 2021 Feb 2;52(1):3–22. doi: 10.1111/sifp.12144

TABLE 5.

Effects of free access to a broad contraceptive method mix among key sub-groups

(1) (2) (3)
All women Current contraceptive users
Modern Contraceptive Use LARC Use Decision-Making
Panel A: Heterogeneity by marital status
Treat × Marstat1 (married, cohabiting, & divorced) 0.028
[−0.002,0.059]
−0.016
[−0.059,0.028]
0.034
[−0.011,0.080]
Treat × Marstat0 (never married & widow) 0.055*
[0.001,0.108]
−0.112
[−0.235,0.011]
−0.016
[−0.091,0.059]
p-value 0.428 0.120 0.247
Panel B: Heterogeneity by age
Treat × Older (age > 19.5) 0.066***
[0.038,0.094]
−0.025
[−0.068,0.019]
0.025
[−0.017,0.067]
Treat × Younger (age < 19.5) −0.183***
[−0.238,−0.128]
−0.079
[−0.169,0.011]
−0.004
[−0.069,0.062]
p-value 0.000 0.195 0.275
Panel C: Heterogeneity by knowledge
Treat × High (knowledge score > −1.14) 0.042***
[0.020,0.065]
−0.025
[−0.070,0.020]
0.019
[−0.028,0.066]
Treat × Low (knowledge score < −1.14) −0.043
[−0.118,0.033]
−0.022
[−0.133,0.088]
0.019
[−0.045,0.082]
p-value 0.029 0.967 0.997
Panel D: Heterogeneity by education
Treat × Edu1 (at least some schooling) 0.043***
[0.021,0.065]
−0.026
[−0.073,0.021]
0.022
[−0.027,0.072]
Treat × Edu0 (no schooling) −0.047
[−0.109,0.015]
−0.048
[−0.140,0.044]
0.009
[−0.057,0.075]
p-value 0.000 0.371 0.670
Panel E: Heterogeneity by wealth
Treat × Wealth1 (above lowest quintile) 0.029*
[0.005,0.054]
−0.022
[−0.067,0.023]
0.027
[−0.021,0.075]
Treat × Wealth0 (lowest quintile) 0.003
[−0.064,0.071]
−0.099*
[−0.194,−0.003]
−0.038
[−0.099,0.023]
p-value 0.061 0.115 0.171
Number of observations 14917 4229 4229

95% confidence intervals in brackets

LARC: long-acting reversible contraceptives, including implants and intrauterine devices. Decision-making: whether respondent participated in the decision on contraceptive method. This table reports the average marginal effects of the treatment (free access to a broad contraceptive method mix) on subgroups of women indicated in each panel. The subgroups were identified by a recursive partitioning machine learning analysis. A random half of the full data were used to identify the source of heterogeneity and the other half were used to estimate treatment effects reported in this table. Logistic regression models included individual covariates and country fixed effects. Propensity weights generated from generalized boosted models were used to adjust for observed differences by treatment status. The p-value rows report the two-sided p-value from an F-test of equality of the treatment effects for the two sub-groups indicated in each panel.

*

p < 0.05,

**

p < 0.01,

***

p < 0.001