Table 4 Chance of overeducation compared to a correct skill match or undereducation, odds ratios, standard errors in brackets
From: Skill mismatch among migrant workers: evidence from a large multi-country dataset
(Demographic variables) | (Demo and theoretical) | (Demo and theoretical) | (Demo, theoretical and MIGRATION) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
MIGRANT | 1.109054 | *** | (0.014) | |||||||||
Age | 0.9882 | *** | (0.001) | 0.9941 | *** | (0.001) | 0.9939876 | *** | (0.000) | |||
Breaks | 1.0934 | *** | (0.009) | 1.0746 | *** | (0.002) | 1.0744215 | *** | (0.002) | |||
Second generation | 1.1583 | * | (0.070) | 1.1143 | *** | (0.026) | 1.1247619 | *** | (0.026) | |||
Job prospects | 0.4013 | *** | (0.027) | |||||||||
Gender (female = 1) | 1.3497 | *** | (0.013) | 1.2388 | *** | (0.027) | 1.1614 | *** | (0.008) | 1.1615692 | *** | (0.008) |
Continent (ref. EU15) | ||||||||||||
EU12 | 1.0088 | (0.028) | 0.8973 | ** | (0.042) | 1.0344 | . | (0.018) | 1.0400934 | * | (0.018) | |
Africa | 0.9394 | ** | (0.029) | 0.9463 | (0.066) | 0.9466 | ** | (0.018) | 0.9448265 | ** | (0.018) | |
Latin America | 1.2374 | *** | (0.018) | 1.0673 | * | (0.033) | 1.2084 | *** | (0.011) | 1.2164861 | *** | (0.011) |
Asia | 1.2717 | *** | (0.021) | 1.5446 | *** | (0.101) | 1.2976 | *** | (0.014) | 1.3027477 | *** | (0.014) |
North America and Oceania | 1.0676 | (0.053) | 1.3034 | . | (0.150) | 1.0099 | (0.033) | 0.9953363 | (0.033) | |||
Europe non-EU | 1.9529 | *** | (0.018) | 1.4598 | *** | (0.044) | 1.6604 | *** | (0.011) | 1.6590651 | *** | (0.010) |
Education (ref. ISCED10) | ||||||||||||
ISCED 20 | 1.3761 | *** | (0.061) | 1.5837 | *** | (0.137) | 1.3741 | *** | (0.035) | 1.3743994 | *** | (0.035) |
ISCED 30 | 1.3765 | *** | (0.059) | 1.3747 | * | (0.136) | 1.3997 | *** | (0.034) | 1.3981505 | *** | (0.034) |
ISCED 40 | 1.6345 | *** | (0.063) | 1.9162 | *** | (0.141) | 1.6286 | *** | (0.036) | 1.6268751 | *** | (0.036) |
ISCED 50 | 1.7580 | *** | (0.059) | 2.1475 | *** | (0.134) | 1.8736 | *** | (0.034) | 1.8685825 | *** | (0.034) |
ISCED 60 | 1.8940 | *** | (0.064) | 2.3162 | *** | (0.144) | 1.9215 | *** | (0.037) | 1.9129916 | *** | (0.037) |
Corporate Hierarchy | ||||||||||||
CH2 | 0.4914 | *** | (0.060) | 0.5024 | *** | (0.122) | 0.6957 | *** | (0.037) | 0.6966276 | *** | (0.037) |
CH3 | 0.5743 | *** | (0.072) | 0.5946 | *** | (0.146) | 0.7564 | *** | (0.044) | 0.7577056 | *** | (0.044) |
CH4 | 0.3733 | *** | (0.064) | 0.3494 | *** | (0.133) | 0.4760 | *** | (0.041) | 0.4765683 | *** | (0.041) |
CH5 | 0.3392 | *** | (0.064) | 0.3714 | *** | (0.130) | 0.4551 | *** | (0.042) | 0.4553189 | *** | (0.043) |
CH6 | 0.3225 | *** | (0.079) | 0.4210 | *** | (0.152) | 0.3892 | *** | (0.055) | 0.3894885 | *** | (0.055) |
Firm size (ref. 1–10) | ||||||||||||
size 11-50 | 0.9187 | *** | (0.018) | 0.9349 | . | (0.036) | 0.9470 | *** | (0.011) | 0.947486 | *** | (0.011) |
size 51-100 | 0.8855 | *** | (0.023) | 0.9541 | (0.045) | 0.9037 | *** | (0.014) | 0.904243 | *** | (0.014) | |
size 101-500 | 0.8694 | *** | (0.019) | 0.9553 | (0.039) | 0.9038 | *** | (0.012) | 0.9043934 | *** | (0.012) | |
Size 500+ | 0.8035 | *** | (0.019) | 0.8641 | *** | (0.041) | 0.9015 | *** | (0.012) | 0.9018726 | *** | (0.012) |
Industry (ref. agr., man. and constr.) | ||||||||||||
Trade, transp. and hotels | 1.6906 | *** | (0.023) | 1.5635 | *** | (0.050) | ||||||
Commercial services | 1.4966 | *** | (0.018) | 1.4566 | *** | (0.040) | ||||||
No. of observations | 145 684 | 34 447 | 368 564 | 368 564 | ||||||||
Nagelkerke presudo R2 | 0.045 | 0.097 | 0.034 | 0.034 | ||||||||
LR chi2 | 4 475.61 | 2369.28 | 8673.66 | 8726.23 | ||||||||
Pr(> chi2) | <0.0001 | <0.0001 | <0.0001 | <0.0001 | ||||||||