Table 6 Regression coefficients for model predicting the residuals from the total scenario regressions in Table 4
From: Analysing global professional gender gaps using LinkedIn advertising data
Dependent variable: Residuals from Total scenarios in Table 4 | |||
|---|---|---|---|
ILO professional GGI | ILO total management GGI | ILO senior/middle management GGI | |
Intercept | 0.14 | −0.12 | −0.63∗∗ |
(0.19) | (0.29) | (0.31) | |
LinkedIn penetration GGI | −0.67∗∗∗ | −0.24∗∗∗ | −0.40∗∗∗ |
(0.04) | (0.07) | (0.09) | |
GGG labor force GGI | 0.15 | −0.28∗∗ | |
(0.13) | (0.14) | ||
Proportion LinkedIn users aged 18–24 | 0.47 | ||
(0.29) | |||
Internet access GGI | 0.84∗∗∗ | 1.36∗∗∗ | 1.00∗∗∗ |
(0.14) | (0.27) | (0.25) | |
HDI (male) | −0.32∗∗ | −0.51∗ | |
(0.15) | (0.30) | ||
LinkedIn penetration | −0.09∗∗∗ | ||
(0.02) | |||
GGG educational attainment GGI | −0.02 | ||
(0.27) | |||
GGG secondary education enrollment GGI | −0.63∗∗∗ | 0.50∗ | |
(0.23) | (0.27) | ||
Internet penetration | 0.08 | −0.25∗∗ | −0.30∗∗∗ |
(0.07) | (0.13) | (0.10) | |
GGG tertiary education enrollment GGI | 0.04 | ||
(0.04) | |||
N | 129 | 120 | 70 |
Adj. \(R^{2}\) | 0.76 | 0.35 | 0.47 |