Research Article

Social Capital and Digital Divide: Implications for Mobile Health Policy in Developing Countries

Table 3

Estimates of effect on digital access.

Internet accessMobile phone access
Probit model (1)Two-step IV-probit model (2)IV-probit model (3)Probit model (4)Two-step IV-probit model (5)IV-probit model (6)

PCE(Log)0.100.060.040.120.060.04
(8.72)(4.08)(2.59)(18.14)(4.62)(3.17)
PrimaryEDU0.230.190.140.360.310.19
(5.41)(4.13)(3.33)(11.74)(7.38)(5.06)
MediumEDU0.500.460.340.570.510.32
(11.28)(9.25)(5.51)(14.78)(10.19)(6.03)
HighEDU1.110.880.660.610.240.15
(12.99)(7.67)(4.28)(5.57)(1.73)(1.43)
Residence0.640.790.600.030.260.16
(18.85)(14.07)(12.29)(0.90)(4.20)(5.49)
ChronicDisease−0.16−0.22−0.17−0.02−0.11−0.07
(−4.14)(−4.97)(−5.21)(−0.50)(−2.50)(−2.65)
SocialCapital0.020.790.640.101.290.81
(1.52)(3.72)(6.50)(7.87)(5.88)(17.19)
Sex0.140.110.080.070.020.01
(4.99)(3.32)(2.73)(2.83)(0.57)(0.55)
Aged 60–74−0.31−0.27−0.20−0.49−0.41−0.26
(−9.19)(−6.68)(−4.33)(−17.78)(−10.54)(−5.77)
Aged above 75−0.34−0.38−0.29−0.93−0.99−0.61
(−5.06)(−5.18)(−4.81)(−20.72)(−15.76)(−8.50)
HouseholdSize0.040.060.050.110.140.09
(4.31)(4.91)(5.58)(9.13)(9.72)(8.24)
CoresidenceChildren0.620.740.560.390.580.36
(15.78)(13.46)(10.99)(10.42)(10.10)(10.28)
Coresidence grand children0.280.170.120.450.280.17
(4.17)(2.15)(1.80)(6.96)(3.27)(2.67)
Marital0.140.070.050.10−0.01−0.00
(2.82)(1.29)(1.15)(2.73)(−0.13)(−0.15)
CommunityInfrastructure0.090.080.060.020.010.01
(22.42)(18.93)(7.19)(4.86)(2.87)(2.60)
PublicInvestment(Log)0.080.140.100.050.130.08
(7.54)(7.13)(10.79)(4.68)(6.40)(9.16)
Constant−3.22−3.79−2.89−0.92−1.82−1.13
(−29.71)(−19.86)(−12.50)(−12.59)(−9.66)(−18.25)
athrho−0.79−1.06
(−4.29)(−7.30)
lnsigma0.050.05
(6.28)(6.28)
Wald test of exogeneity: chi2(1) = 18.41 prob > chi2 = 0.0000Wald test of exogeneity: chi2(1) = 53.33 prob > chi2 = 0.0000
Obs.163161631616316163161631616316
Pseudo R20.26.z.z0.20.z.z

t-values are in parentheses. , , .