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Table 1 Random effects estimations—dependent variable: average cantonal requirements profile in chosen VET occupations (mean: 37.80; sd within: 0.39)

From: Brain drain from vocational to academic education at upper-secondary level? An empirical analysis for Switzerland

Variables (1)
Without industry controls
(2)
With industry controls
Academic education rate 2.476 1.765
(2.171) (2.448)
Percentage of immigrants 2.038 − 1.351
(2.248) (4.039)
Percentage of school-based VET (entries) 2.140** 3.314***
(0.874) (0.973)
Percentage of graduates from compulsory school 60.45 7.139
(77.49) (106.1)
Percentage of employees in agriculture   3.350
  (17.66)
Percentage of employees in production   0.674
  (10.27)
Percentage of employees in other services   − 5.864
  (10.65)
Percentage of employees in construction   − 7.942
  (13.61)
Percentage of employees in retail   13.27
  (11.39)
Percentage of employees in financial services and insurance   17.30
  (11.71)
Percentage of employees in public administration and education   − 0.785
  (15.86)
Constant 34.75*** 35.77***
(1.301) (9.066)
Language region Yes Yes
Percentage of imputed values Yes Yes
Year dummies Yes Yes
Observations 182 156
R2 0.278 0.292
Number of cantons 26 26
  1. This table shows coefficients from a random effects regression. Standard errors in parentheses. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. The reference category for the share of employees in the different sectors is the health sector
  2. To show that the results are not driven by outliers, two different bivariate illustrations of the relationships between the average cantonal requirements profile and the academic education rate, as well as the percentage of school-based VET appear in Appendix A, Figs. 3 and 4
  3. We additionally tested these regressions with FE models, with very similar results