From: Skill mismatch among migrant workers: evidence from a large multi-country dataset
Whole sample | Netherlands | Germany | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Probability of being a migrant | Estimate | Std. | Estimate | Std. | Estimate | Std. | Estimate | Std. | ||||
Age | 0.010 | *** | (0.000) | 0.010 | *** | (0.000) | 0.000 | (0.001) | −0.013 | *** | (0.001) | |
Gender (female = 1) | 0.026 | *** | (0.006) | 0.014 | * | (0.006) | 0.137 | *** | (0.017) | −0.007 | (0.027) | |
Education | YES | YES | YES | YES | ||||||||
Continent of provenance (ref. EU15) | ||||||||||||
EU12 | 0.376 | *** | (0.013) | 0.701 | *** | (0.039) | 0.842 | *** | (0.050) | |||
Africa | 0.330 | *** | (0.013) | 0.760 | *** | (0.033) | 0.256 | *** | (0.073) | |||
C-S America | 0.056 | *** | (0.010) | 0.561 | *** | (0.023) | 0.003 | (0.044) | ||||
Asia | 0.457 | *** | (0.010) | 0.742 | *** | (0.026) | 0.438 | *** | (0.041) | |||
NAO | 0.464 | *** | (0.022) | 0.969 | *** | (0.059) | 0.281 | * | (0.113) | |||
Eur not-EU | 0.428 | *** | (0.009) | −0.104 | ** | (0.031) | 0.277 | *** | (0.034) | |||
Intercept | −1.937 | *** | (0.026) | −2.137 | *** | (0.027) | −1.317 | *** | (0.068) | −1.150 | *** | (0.059) |
Probability of overeducation | ||||||||||||
Age | 0.000 | (0.001) | −0.001 | (0.001) | −0.003 | (0.002) | 0.009 | * | (0.004) | |||
Gender (female = 1) | 0.096 | *** | (0.016) | 0.088 | *** | (0.016) | 0.180 | *** | (0.044) | 0.258 | ** | (0.075) |
Breaks | 0.038 | *** | (0.004) | 0.036 | *** | (0.004) | 0.065 | *** | (0.012) | 0.064 | * | (0.020) |
Education (ref. ISCED10) | YES | YES | YES | YES | ||||||||
Corporate hierarchy | YES | YES | YES | YES | ||||||||
Firmsize | YES | YES | YES | YES | ||||||||
Intercept | −0.413 | *** | (0.010) | −0.063 | (0.134) | −0.956 | *** | (0.227) | −0.933 | ** | (0.287) | |
ath(rho) | −0.232 | *** | (0.034) | −0.381 | *** | (0.049) | 0.013 | (0.076) | −0.148 | (0.147) | ||
rho | −0.228 | −0.364 | 0.013 | −0.147 | ||||||||
N | 368564 | 368564 | 53583 | 22868 |