Table 4 Regression coefficients from the models predicting ILO GGIs from LinkedIn overall GGI for all countries (total) and countries with low (ILO GGI < median ILO GGI) and high (ILO GGI ≥ median ILO GGI) professional gender inequality

Intercept

0.29^∗∗∗

0.28^∗∗∗

0.93^∗∗∗

0.27^∗∗∗

0.16^∗∗∗

0.86^∗∗∗

0.27^∗∗∗

0.16^∗∗∗

0.73^∗∗∗

(0.05)

(0.04)

(0.08)

(0.06)

(0.03)

(0.10)

(0.06)

(0.04)

(0.08)

LinkedIn overall GGI

0.79^∗∗∗

0.56^∗∗∗

0.24^∗∗∗

0.36^∗∗∗

0.23^∗∗∗

−0.12

0.29^∗∗∗

0.26^∗∗∗

−0.07

(0.06)

(0.07)

(0.08)

(0.07)

(0.05)

(0.10)

(0.07)

(0.05)

(0.08)

N

185

92

93

167

82

85

89

44

45

N (pred)

234

92

93

234

82

85

234

44

45

\(R^{2}\)

0.50

0.45

0.09

0.13

0.24

0.01

0.17

0.36

0.02

5-fold CV

CV \(R^{2}\)

0.52

0.50

0.14

0.20

0.31

0.07

0.24

0.37

0.11

MAE

0.17

0.12

0.14

0.20

0.10

0.20

0.15

0.08

0.13

RMSE

0.24

0.15

0.19

0.29

0.11

0.27

0.20

0.09

0.17