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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

From: Analysing global professional gender gaps using LinkedIn advertising data

  Dependent variable:
  ILO professional GGI ILO total management GGI ILO senior/middle management GGI
  Total Low High Total Low High Total Low High
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
  1. Note: p<0.1; p<0.05; p<0.01.