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Table 3 Predicting graduates’ income 5 years after graduation

From: Privilege travels: migration and labour market outcomes of Southern Italian graduates

  Model 1 baseline Model 2 + migration for study
Estimate S.E p value Estimate S.E p value
Parental education
 Both with higher education (ref)       
 At least one with higher educated − 0.040 0.018 0.036 − 0.032 0.018 0.080
 At least one with high school − 0.050 0.016 0.006 − 0.035 0.016 0.029
 Both with less than high school − 0.081 0.018 0.000 − 0.065 0.017 0.000
Migration study     0.154 0.010 0.000
Field of study
 Law (ref)       
 Agriculture 0.137 0.040 0.001 0.133 0.040 0.001
 Architecture 0.075 0.029 0.010 0.062 0.029 0.032
 Chemistry and pharmacy 0.399 0.024 0.000 0.401 0.024 0.000
 Economics/statistics 0.304 0.027 0.000 0.299 0.027 0.000
 Sports science and physical education − 0.179 0.053 0.001 − 0.204 0.053 0.000
 Biology and geography 0.161 0.034 0.000 0.155 0.034 0.000
 Engineering 0.486 0.026 0.000 0.479 0.026 0.000
 Education − 0.038 0.041 0.362 − 0.038 0.041 0.354
 Literature 0.026 0.032 0.414 − 0.007 0.032 0.829
 Linguistics 0.119 0.034 0.001 0.109 0.034 0.002
 Medicine 0.455 0.034 0.000 0.443 0.034 0.000
 Political science/social science 0.180 0.028 0.000 0.150 0.028 0.000
 Psychology − 0.148 0.032 0.000 − 0.168 0.032 0.000
 Natural science/mathematics/physics 0.358 0.036 0.000 0.371 0.036 0.000
Gender
 Men (ref)       
 Women − 0.195 0.011 0.000 − 0.190 0.011 0.000
Constant 7.800 0.112 0.000 7.668 0.112 0.000
R-Squared 0.195   0.210  
N 11,192   11,192  
  1. Linear probability models with robust standard error; All models control also for: age, degree type, secondary school, grades secondary school,grades university