Table 2

Effect of Predicted Unemployment Rate at Graduation (ϕcj) and Pilot Intensity (Pcj) on High School Progression?

Dependent Variable
p(Enrolls in Upp. Sec. School)p(Enrolls in Voc. Track)p(Delays Enrollment in Voc. Track)p(Drops Out from Voc. Track)GPA Rank for Dropouts
(1)(2)(3)(4)(5)
Predicted unemployment rate at grad. (ϕcj)0.000−0.000−0.000−0.0010.257
(0.002)(0.002)(0.001)(0.002)(0.600)
Pilot intensity (Pcj)0.0010.0110.0050.014−7.326*
(0.013)(0.016)(0.005)(0.017)(3.972)
GPA rank in 9th grade0.004***−0.010***−0.000***−0.003***
(0.000)(0.000)(0.000)(0.000)
Born in Nordic country0.017***0.073***−0.132***−0.0023.307**
(0.005)(0.005)(0.009)(0.007)(1.407)
Cohort FE
Municipality FE
Mean of dep. variable0.860.380.020.1231.12
Individuals255,471200,50278,64177,1928,946
  • Notes: Marginal effects are presented. Standard errors (in parentheses) are clustered at the municipality * cohort (j × c) level. Model 1 is estimated using the sample of students observed three years after graduation from compulsory school, including dropouts. Since not all students in compulsory school enrolled in upper secondary school, Pcj and ϕcj are measured at the municipality * cohort (j × c) level, where j stands for municipality of residence at age 16 (as information on the municipality of residence during the last year of compulsory schooling is only available from the upper secondary application register) and c stands for the year the individual finishes compulsory school. Models 2–5 are estimated using the sample of students observed in the year of (predicted) graduation, including dropouts. We estimate Model 2 conditional on enrolling in upper secondary school at age 16, Model 3 conditional on enrolling in vocational studies, Model 4 conditional on enrolling in vocational studies at age 16, and Model 5 conditional on enrolling in vocational studies at age 16 and dropping out. Unconditional estimations of Models 2–4 yield very similar results (not shown). Significance: *p < 0.10, **p < 0.05, ***p < 0.01.