From: Gender wage gap in European emerging markets: a meta-analytic perspective
Estimator | Unrestricted WLS | WLS with bootstrapped standard errors | Cluster-robust | Random-effects panel ML | IV |
---|---|---|---|---|---|
WLS | |||||
 | [1] | [2] | [3] | [4] | [5] |
(a). FAT–PET test (Equation: t = γ0 + γ1(1/SE) + v) |  | ||||
 Intercept (FAT: H0: γ 0 = 0) | − 6.4514*** (1.038) | − 6.4514*** (0.996) | − 6.4514* ( 3.831) | − 9.4834 ( 7.115) | − 3.4118** (1.734) |
 1/SE (PET: H0: γ 1 = 0) | − 0.0875*** (0.013) | − 0.0875*** (0.013) | − 0.0875* (0.049) | − 0.0569** (0.027) | − 0.1076*** (0.009) |
 K | 670 | 670 | 670 | 670 | 670 |
 R2 | 0.4 | 0.399 | 0.4 | 0.4 | 0.379 |
Estimator | Unrestricted WLS | WLS with bootstrapped standard errors | Cluster-robust | Random-effects panel ML | IV |
---|---|---|---|---|---|
WLS | |||||
Model | [6] | [7] | [8] | [9] | [10] |
(b). EESE approach (Equation: t = γ 0SE + γ 1(1/SE) + v) | |||||
 SE | − 111.5465*** (18.967) | − 111.5465*** (16.454) | − 111.547 (68.935) | 165.6966*** (31.308) | 388.5471 (494.315) |
 1/SE (H0: γ 1 = 0) | − 0.0970*** (0.012) | − 0.0970*** (0.011) | − 0.0970** (0.044) | − 0.0513*** (0.006) | − 0.4378 (0.32) |
 K | 670 | 670 | 670 | 670 | 670 |
 R2 | 0.524 | 0.523 | 0.524 | – | - |